The Model Context Protocol (MCP): architecture, concepts and ecosystem

The Model Context Protocol (MCP) is an open standard designed to fundamentally rethink how artificial intelligence applications interact with their digital environments.
Introduced by Anthropic in November 2024, MCP aims to unify access to language models (LLMs) to external data sources and tools, replacing fragmented and proprietary integrations with universal, secure and structured interface.

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Where businesses previously had to develop a separate connector for each model and tool combination (creating a type complexity N×M), MCP transforms this problem into a much more manageable architecture, such as M+N : each model implements a single client, each tool a single server, and both communicate according to a common grammar.

Fundamental architecture

The MCP architecture is based on a standardized client—server model, organized around three main components that work together to provide rich context to language models:

MCP Host

The host acts as the central orchestrator of the system. This is the container application — for example Claude Desktop, Visual Studio Code, Cursor, or any other AI environment — that manages the connections between the various components.

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Its main functions include:

• The creation and supervision of MCP clients,
• The permission management And user permissions,
• The application of security policies and isolation rules,
• And the coordination of exchanges between LLMs and external > servers.

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The host is in a way the conductor contextual networking: it ensures that each interaction is traceable, authorized and secure.

MCP Client

The client is the active bridge between the host and the MCP servers.
It maintains a 1:1 isolated connection with each server and ensures the consistency of exchanges via a persistent session with status.

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Its responsibilities include:

• The negotiation of the protocol and the discovery of capabilities  from the server;
• The bi-directional message management and events;
• The preservation of security borders between servers;
• And the maintenance of subscriptions and notifications, guaranteeing smooth and continuous communication.

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The client therefore acts as a intelligent intermediation layer, ensuring that each server can communicate with the LLM without directly exposing its data or compromising the integrity of the system.

MCP Server

The MCP server is a lightweight program dedicated to a specific function.
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He exposes his capacities — resources, tools, prompts or sampling requests — according to standardized protocol primitives.
A server can function in a Independent, locally (isolated process) or remotely (HTTP/SSE service).
Each server stays responsible for a specific functional perimeter : it can, for example, provide access to a database, a business API, a file system, or a scientific computing environment.

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Thanks to this approach modular and decoupled, the same host can connect to several MCP servers in parallel, each bringing a different skill or type of resource, while maintaining insulation, safety and traceability exchanges between them.

Protocol Structure and JSON-RPC Messages

The Model Context Protocol (MCP) relies on JSON-RPC 2.0 as the technical foundation for all exchanges between clients and servers. This choice is based on a logic of simplicity and universality: JSON-RPC provides a message format that is lightweight, human-readable, language-agnostic, and already widely adopted in the software ecosystem. MCP extends it with specific conventions to guarantee the traceability and robustness of interactions.

The three fundamental message types

1. Requests
   ‍These are the initial messages sent to execute an operation.Each request contains:
   • a unique identifier (string or integer, never null);
   • a method to invoke, such as tools/call or resources/read;
   • and optional parameters.The ID is used to link each request to its response and must never be reused within a given session.
2. Responses
   Responses reuse the ID of the corresponding request and include either a result field (success) or an error field (failure)—but never both. Error codes are standardized as integers and allow for precise diagnosis of incidents.
3. ‍Notifications Unidirectional messages that do not require a response, notifications inform of a state change or an event without blocking the main execution flow. They are commonly used to signal resource updates, session modifications, or the arrival of new real-time events.

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The complete specification of the protocol is written in TypeScript, which serves as the source of truth, and a JSON Schema version is automatically generated to facilitate integration with validation, documentation, or SDK generation tools and client/server implementations in other languages.

Lifecycle Management and Capability Negotiation

MCP defines a strict lifecycle management for connections between clients and servers to ensure session stability and shared state coherence. This cycle takes place in three distinct phases:

1. Initialization Phase

Upon connection establishment, the client sends an initialize request to the server. This step allows:

• negotiating the protocol version;
• exchanging the supported capabilities on both sides (available tools, accessible resources, message formats).
  The server responds by specifying its own capabilities, after which the client emits an initialized notification to confirm that the session is operational. This phase lays the groundwork for a contractual and verifiable communication.

2. Operation Phase

Once the session is established, exchanges become bidirectional: requests, responses, and notifications flow according to the negotiated capabilities.

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It is during this active phase that:

• tools are invoked;
• resources are read or written;
• and prompts, requests, or contextual actions are executed.
  Each interaction respects the authorization, audit, and isolation logic specific to the session.

3. Shutdown Phase

When the session is no longer needed, closure is gracefully initiated by the client or the server.

This final step releases allocated resources, properly closes open channels, and prevents the appearance of orphaned connections that could unnecessarily consume resources or create security vulnerabilities.

Capability Negotiation

One of MCP's strengths lies in its dynamic discovery and negotiation mechanism.

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Upon initialization, each participant announces:

• the methods it supports;
• the data formats it accepts;
• and the extensions it supports.

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The client and server thus build an explicit session contract, ensuring that only mutually compatible functionalities are used. This process prevents runtime errors, improves resilience, and makes each session predictable, traceable, and extensible.

Transport Layer: STDIO, HTTP, and SSE

The Model Context Protocol (MCP) was designed to adapt to a wide variety of environments and integration constraints.

For this purpose, it supports several transport mechanisms for communication between clients and servers, each having specific advantages depending on the usage scenario.

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STDIO (Standard Input/Output) — the quintessential local transport

The STDIO mode relies on simple and direct operation: the client launches the MCP server as a child process and communicates with it via the standard stdin and stdout streams.

Each message is delimited by a newline and formatted according to the JSON-RPC 2.0 specification.

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This mode has several major advantages:

• Extremely low latency, with no network passage.
• Minimal configuration, ideal for development environments or embedded integrations.
• Enhanced security, as no network exposure is necessary: communication remains confined to the local context.
• 1:1 relationship between client and server, ensuring total isolation of each session.

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It is the preferred transport for:

• local integrations in applications such as Claude Desktop or VS Code;
• Command Line Interface (CLI) tools;
• and sensitive operations requiring strict control over data flows.

HTTP with Streamable — the recommended modern network transport

The HTTP with Streamable transport transforms the MCP server into a web service accessible via URL.

Unlike the STDIO model, where each client has its own process, an HTTP server can manage multiple clients simultaneously, fostering scalability and distributed deployment.

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The Streamable HTTP protocol introduces full bidirectional communication between client and server, while supporting:

• streaming responses, allowing a continuous flow of information (useful for tracking long tasks);
• concurrent management of multiple active sessions;
• and fluid integration with existing web infrastructures (API Gateway, reverse proxy, load balancer).

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This mode is recommended for:

• remote or cloud deployments;
• multi-client architectures (multiple agents connected to the same service);
• and network applications that require centralized supervision or orchestration.

SSE (Server-Sent Events) — inherited compatibility

Transport via Server-Sent Events (SSE) is historically the first method used by MCP for HTTP communication.

It allows unidirectional streaming from the server to the client, but does not support full bidirectional exchange.

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While still maintained for backward compatibility reasons, SSE is now deprecated for new projects. Developers are encouraged to adopt HTTP Streamable, which offers greater flexibility, performance, and security.

Choice of Transport: a Question of Context

The choice of transport mechanism essentially depends on the deployment scenario and operational priorities:

• STDIO is perfectly suited for local integrations, CLI tools, and environments where simplicity and security are paramount.
• HTTP Streamable is essential for network architectures, multi-agent environments, and large-scale collaborative uses.
• SSE, finally, remains useful for maintaining compatibility with older clients, but is no longer recommended for modern deployments.

The Three Fundamental Primitives: Resources, Prompts, and Tools

At the heart of the Model Context Protocol (MCP) are three essential primitives—Resources, Prompts, and Tools—that allow servers to offer rich, explainable, and contextualized interactions to language models.

These three components define the common grammar between an LLM and its digital environment, structuring reading, understanding, and action.

Resources — the informational foundation

Resources represent all the data that a model can access, whether local, remote, or dynamically generated.

Each resource is identified by a URI (Uniform Resource Identifier), ensuring a clear structure and standardized access, regardless of the content type.

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Examples of URI formats:

• file:///home/user/projects/api/README.md — local file;
• postgres://localhost/customers/table/users — table in a PostgreSQL database;
• weather://current/san-francisco — dynamic resource linked to a weather API.

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Servers expose these resources via the resources/list endpoint, while hosts can request them to be read using resources/read.

Each resource is accompanied by descriptive metadata (MIME type, description, update date, access rights) allowing users and models to understand its content and context before use.

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Resources can be:

• static, like a document or versioned source code;
• or dynamic, like the latest entries in a system log or an IoT stream.

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Thus, the LLM is no longer confined to its training data: it accesses a universe of living, readable, and traceable information.

Prompts — encapsulated expertise

Prompts constitute the second key primitive of the protocol.

They offer reusable instruction templates encapsulating the expertise of a domain or the logic of a complex task.

Each prompt acts as an operational guide that the model can invoke with precise parameters, ensuring consistency, reliability, and homogeneity of responses.

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Typical examples:

• A database server can offer:
  
  • analyze-schema (analyze a schema),
  • debug-slow-query (diagnose a slow query),
  • generate-migration (create a migration script).
• A Kubernetes server can offer prompts to troubleshoot a cluster or analyze a deployment.
• A code review server can expose prompts adapted to the team's internal style guides.

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Prompts can accept dynamic arguments: for example, analyze-table takes the name of a table as input, then returns a structured diagnosis including schema, indexes, and foreign key relationships.

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In summary, prompts constitute the protocol's business memory—a way to industrialize human expertise in the form of standardized and exploitable instructions for models.

Tools — the interface with the real world

Finally, Tools are the executable functions that a model can call to perform concrete actions in its environment.

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Each tool is defined by:

• an explicit name,
• a clear description of its usage,
• a JSON schema detailing the input parameters (name, type, constraints, default values),
• and a handler—the code that actually executes the business logic.

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Invocation is done via the tools/call method, where the client provides the tool name and the expected arguments.

The server executes the function, then returns the structured result (success or error).

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These tools allow the LLM to move beyond simple textual generation to interact operationally: execute commands, modify a database, launch a simulation, or orchestrate other services.

A Rich and Coherent Ecosystem

By combining these three primitives :

• Resources for context,
• Prompts for guidance,
• Tools for action

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MCP offers a complete interface between the model's thought and the digital world.

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This tripartition transforms the language model into a connected cognitive actor, capable of reading, understanding, reasoning, and acting in a governable manner, within an ecosystem that is standardized, extensible, and traceable.

Advanced Features: Sampling and Elicitation

Beyond its three fundamental primitives (Resources, Prompts, and Tools), the Model Context Protocol (MCP) introduces advanced bidirectional mechanisms that allow servers to become true intelligent agents, capable not only of responding to requests but also of initiating them. These capabilities open the way to a more natural and autonomous interaction between the system's components, while maintaining explicit human control at every critical step.

Sampling: the inversion of the decisional flow

The sampling mechanism marks a major evolution in the protocol's interaction philosophy.

Traditionally, it is the clients that query the language models. With sampling, MCP servers can themselves request LLM completions, thus inverting the classic communication flow.

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This mechanism is used when a server needs to:

• make a complex decision involving reasoning
• generate a response beyond deterministic rules;
• or understand a semantic context that only a language model can interpret.

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The execution flow follows a rigorously controlled sequence:

1. The server sends a sampling/createMessage request to the client, containing the conversation messages, model preferences (if any), system instructions, and the required context.
2. The client receives this request, validates it, and displays to the user what the server wants to transmit to the model.
   → Human Checkpoint n°1: the user can edit, approve, or reject the proposed prompt.
3. If the prompt is approved, the client sends it to the LLM, receives the completion, and presents it to the user again.
   → Human Checkpoint n°2: the user can correct, validate, or refuse the generated response.
4. Upon approval, the final completion is returned to the server, which can then continue its processing.

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This dual human control guarantees total traceability of the reasoning, while allowing servers to leverage the power of language models without loss of governance.

Sampling thus embodies a balanced compromise between agentic autonomy and active human supervision—a fundamental principle of MCP design.

Elicitation: the interactive loop with the user

The second advanced capability, known as elicitation, allows MCP servers to request complementary information from the user when a necessary piece of data for the execution of a task is missing or ambiguous.

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This functionality transforms the server from a simple executor into a dialogic actor, capable of maintaining a contextual and adaptive exchange:

• if a command requires a missing parameter (e.g., file name, date range, resource identifier), the server can directly query the user;
• the client relays this request in a clear and validatable form;
• the response is then reinjected into the execution flow.

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Elicitation significantly improves the robustness and usability of MCP-based systems.

Rather than silently failing on input errors, the protocol promotes fluid cooperation between human and machine, where each missing step becomes an opportunity for contextual adjustment.

Metadata Schema and Human Supervision

The Model Context Protocol (MCP) places human supervision at the heart of its design.

Rather than considering governance as an afterthought layer, the protocol integrates it by design: no sensitive action—whether it is data access, code execution, or a request to a language model—can be undertaken without explicit user consent.

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This approach guarantees that final control always belongs to the human, even in environments where multiple AI agents collaborate or make autonomous decisions.

User Consent and Explicit Control

Every operation initiated by an agent must be understood and authorized by the user.

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MCP implementations must therefore:

• provide clear and explicit interfaces allowing the visualization, review, and approval of each action;
• ensure that users know which data is being shared, with which servers, and for what purpose;
• and maintain the possibility to revoke an granted authorization at any time.

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This principle transforms consent into a conscious and traceable act, not a simple checkbox.

Confidentiality and Data Governance

MCP hosts have the obligation to obtain explicit consent before any exposure of user data to an external server.

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No data must be transmitted outside the environment without prior validation, and all data must be protected by:

• granular access controls (based on roles, tasks, or contexts),
• encryption mechanisms for exchanges and storage,
• and audit logs tracing who accessed what, when, and why.

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Thus, MCP ensures complete traceability of data flows and prevents any drift towards opaque or uncontrolled models.

Security of Tools

Tools, capable of executing code or manipulating real resources, represent a particular risk area.

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The protocol therefore imposes a principle of reinforced caution:

• each invocation of a tool must be explicitly approved by the user;
• interfaces must clearly display what the tool is about to execute;
• and hosts must isolate execution environments to prevent side effects or compromise.

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The objective is to ensure that the user fully understands the scope and consequences of each action before authorizing it.

Control of LLM Sampling

LLM sampling requests, which allow a server to request a completion from a language model, are subject to even stricter supervision.

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Users must:

• explicitly validate whether sampling can be performed;
• see the actual prompt that will be sent to the LLM;
• and choose which parts of the response can be returned to the server.

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The protocol deliberately limits the server's visibility into prompts and completions to avoid any leakage of confidential context or unintentional bias.

This approach ensures that sampling remains an assisted process, never automatic.

The Five Layers of Authentication and Authorization

MCP's security model is based on a five-level architecture, designed to ensure complete end-to-end governance:

1. Agent Identity.
   Each AI agent has a unique and traceable identity, allowing its actions and responsibilities to be tracked.
2. Delegator Authentication.
   The human user authenticates to the system and becomes the delegator of authority for the agents they control.
3. Consent Delegation.
   The user defines the scope of authority for each agent—that is, what actions it can undertake, on which resources, and within what temporal or contextual perimeter.
4. MCP Server Access.
   The agent then authenticates with the MCP server, respecting the authorizations delegated by the user.
5. Upstream Services.
   Finally, the external APIs accessed by the server must respect the agent's identity and the inherited permissions from the human delegator.

A Protocol Centered on Trust and Traceability

This hierarchical authentication and explicit consent architecture makes MCP one of the first standards to integrate human governance as a technical component.

Each interaction thus becomes verifiable, reversible, and attributable, laying the groundwork for a Trusted Agentic AI, where autonomy never excludes responsibility.

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MCP Registry: Discovery and Distribution

Officially launched by Anthropic in September 2025, the MCP Registry constitutes a structuring step in the maturation of the Model Context Protocol ecosystem.

This centralized registry acts as a public and interoperable catalog grouping all available MCP servers, thus facilitating their discovery, integration, and governance.

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Designed as a metaregistry, the registry stores neither source code nor binaries, but only the descriptive metadata of the MCP servers.

It positions itself as a universal entry point allowing developers, companies, and orchestration tools to reference and exploit existing servers in a standardized manner.

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Main Features of the Official Registry

• Authoritative Repository
  The MCP Registry is the single source of truth for all officially published public MCP servers.
  It guarantees the consistency, traceability, and reliability of information related to each server available in the ecosystem.
• Community Ownership
  Although initiated by Anthropic, the registry is now collectively managed by the open-source MCP community.
  It benefits from the support of major players such as GitHub, PulseMCP, and Microsoft, who contribute to its maintenance and governance.
  This collaborative approach reinforces its legitimacy as a neutral and sustainable infrastructure.
• Unified Discovery
  Server creators no longer need to multiply publications: a single registration in the MCP Registry is enough to make a server visible across all compatible platforms.
  All consumers—applications, orchestrators, Kins, or multi-agent frameworks—can reference the same canonical data, ensuring total interoperability.
• Standardized Format
  Each registry entry follows the server.json format, which exhaustively describes the elements necessary for the installation and use of a server:
  
  • Unique Identity (e.g., io.github.user/server-name)
  • Available Packages (places to download the server, via npm, PyPI, Docker Hub, etc.)
  • Runtime (execution instructions, arguments, environment variables)
  • Metadata (description, version, capabilities, dependencies, compatibilities)
• This common format simplifies automation, updating, and synchronization between environments.
  

An Interconnected Ecosystem

The MCP Registry is not intended to replace existing third-party registries — such as Smithery, Mastra, Glama.ai, or MCP.so.

On the contrary, it acts as a synchronization core: these platforms can replicate or enrich the metadata of the official registry, while offering their own value-added services (advanced search engines, qualitative evaluation, thematic curation, community scoring, etc.).

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Thus, the official registry becomes a pivot of interoperability between the protocol's base infrastructure and more specialized application layers.

It contributes to making MCP an open, traceable, and transparent ecosystem, where every server can be discovered, evaluated, and integrated with confidence — an App Store for agentics, serving standardization and digital sovereignty.

Link Between MCP and LLM Context Window

One of the most frequent confusions around the Model Context Protocol (MCP) comes from its semantic resemblance to the context window of large language models.

However, these two notions relate to very different technical levels: the first concerns the orchestration infrastructure, and the second the internal limits of the model itself.

The Context Window: An Inherent Model Constraint

The context window refers to the maximum quantity of tokens (words, symbols, or text fragments) that a language model can process simultaneously.

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It is a physical limit, directly linked to the model's architecture and size.

• For example: a model like GPT-4-turbo can process 128,000 tokens, while a lighter model may be limited to 4,000 or 8,000 tokens.
• Beyond this capacity, the model can no longer reason effectively, as it only "sees" a portion of the available text.

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In other words, the context window defines the model's immediate memory — its ability to "remember" what is provided to it in a single request.

The Model Context Protocol: An Orchestration Layer Above

The Model Context Protocol, conversely, has nothing to do with this internal limit.

It is an orchestration protocol that standardizes the way a model accesses its external environment: databases, files, APIs, tools, or other AI agents.

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MCP therefore does not seek to increase the size of the context window, but to intelligently manage the flow of information that enters it.

It defines how and when to provide relevant data to an LLM, while respecting its technical constraints.

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Thus, MCP acts as a context conductor, ensuring that the model:

• receives only the useful information at the right time,
• interacts with reliable and standardized sources,
• and is never overwhelmed by a volume of data greater than what it can process.

Dynamic Contextualization: A Key Contribution of the Protocol

One of MCP's great strengths lies in its ability to enable dynamic and selective contextualization.

Instead of sending all data at once — which would saturate the model's window — the protocol allows servers to:

• selectively provide relevant fragments throughout the reasoning process,
• refresh the context based on the task's status,
• and maintain coherence between successive sessions.

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For cases where the volume of information far exceeds the model's capacity, advanced orchestration patterns can be implemented, such as the sub-context pattern:

1. Divide the data into subsets (chunks);
2. Create a dedicated sub-context for each portion;
3. Process each sub-context independently;
4. Then merge the summaries to produce a consolidated synthesis.

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This mechanism allows for the processing of massive volumes of data while respecting the structural constraints of the LLM.

Reference Implementations and Ecosystem

The success of the Model Context Protocol (MCP) relies on a robust technical ecosystem, designed to accelerate its adoption by the open-source community, software vendors, and large enterprises.

This ecosystem provides both the development tools, the reference implementations, and the industrial integrations necessary to build interoperable and secure agentic applications.

Official SDKs in More Than Ten Languages

To ensure wide and cross-platform adoption, Anthropic and the MCP community maintain official Software Development Kits (SDKs) in more than ten major languages: TypeScript, Python, C\#, Go, Java, Kotlin, PHP, Ruby, Rust, and Swift.

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These SDKs offer:

• high-level abstractions to quickly create compliant MCP servers and clients according to the specification;
• modules for automatic JSON-RPC schema validation;
• and tools to manage capability negotiation, security, and state persistence.

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Thanks to these libraries, a developer can implement an MCP server in a few lines of code, while benefiting from native compliance with the official protocol.

Official GitHub Repository: Reference Servers for All Use Cases

The GitHub repository model-context-protocol/servers centralizes the reference implementations developed by Anthropic and the community.

These servers serve both as pedagogical models and concrete starting points for building new integrations.

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Among the most popular:

• Everything: a versatile test server integrating prompts, resources, and tools.
• Fetch: enables the retrieval and conversion of web content into MCP resources.
• Filesystem: manages secure read and write operations on local file systems.
• Git: provides tools for reading, searching, and manipulating Git repositories.
• Memory: offers persistent memory based on knowledge graphs.
• Sequential Thinking: experiments with reflexive reasoning and task chaining logics.
• Time: manages timezone conversions and complex temporal operations.

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To this foundation is added a galaxy of third-party servers covering almost all enterprise integrations: AWS, Azure, Google Cloud, GitHub, GitLab, Slack, Jira, Salesforce, Stripe, Shopify, PostgreSQL, MongoDB, Elasticsearch, and many others.

MCP Inspector: The Essential Development Tool

The MCP Inspector has become one of the indispensable tools for developers and integrators.

It allows connection to an MCP server and inspection of its advertised capabilities — whether tools, resources, or prompts — without requiring a complete AI client.

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The user can thus:

• visualize the metadata exposed by the server;
• test the endpoints manually;
• simulate requests and verify JSON-RPC responses;
• and diagnose errors before any production deployment.

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In other words, MCP Inspector plays a role equivalent to Postman for REST APIs or GraphiQL for GraphQL: an interactive testing environment essential for understanding, auditing, and securing MCP integrations.

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Thanks to this set — multilingual SDKs, reference servers, industrial integrations, and diagnostic tools — the MCP ecosystem forms a complete, open, and evolving infrastructure, supporting the vision of universal interoperability between AI agents and business environments.

Integration with Orchestration Frameworks

The Model Context Protocol (MCP) does not operate in isolation: it integrates closely with the main agent orchestration frameworks on the market, bringing them standardized interoperability, enhanced security, and unified access to a wide range of tools and resources.

Thanks to these integrations, multi-agent architectures gain in coherence, traceability, and extensibility — three essential conditions for the industrialization of agentic AI.

LangGraph + MCP: State-Aware Orchestration and Universal Interoperability

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LangGraph is a framework designed to orchestrate complex AI agent workflows, relying on a graph of nodes representing decisions, transitions, and shared state between agents.

The integration of MCP into LangGraph allows each agent to consume the capabilities exposed by MCP servers, and thus to access, in a standardized format, external prompts, resources, and tools.

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Concretely, a LangGraph agent can:

• dynamically list available MCP servers;
• invoke their tools as part of its reasoning or a sub-workflow;
• and maintain a persistent contextual state, ensuring the continuity of reasoning between successive calls.

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This hybrid architecture combines LangGraph's structured state management with the functional richness of the MCP network.

Pilot projects already demonstrate decentralized multi-server systems, where LangGraph agents orchestrate distributed workflows, delegating tool execution to remote MCP servers via SSE and STDIO transports.

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Result: an ecosystem where every agent can plan, reason, and act by leveraging the resources of a global contextual mesh.

CrewAI + MCP: Standardizing Access to Enterprise Tools

The CrewAI framework, specializing in the coordination of autonomous agent teams, also integrates natively with MCP via the crewai-tools library and its dedicated adapter, the MCPServerAdapter.

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This adapter ensures:

• secure connection to MCP servers via the chosen transport (STDIO or SSE);
• automatic discovery of available tools on each server;
• and the creation of compatible CrewAI wrappers, exposing these tools in the framework's own language.

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Thus, a CrewAI agent can immediately access a standardized ecosystem of over 50 "enterprise-grade" integrations, notably via services like Klavis or PulseMCP.

This approach unifies connector management and avoids the proliferation of specific scripts for each environment.

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In practice, MCP acts as a universalization layer: CrewAI maintains its multi-agent coordination logic, but offloads tool and resource management to an open and governable infrastructure.

Microsoft AI Foundry + MCP: Unifying Azure AI Services

The Microsoft Azure AI Foundry platform has also adopted MCP as a standardized interface to connect its agents to external tools and services.

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Thanks to this integration, Foundry agents can securely connect to third-party environments, while Copilot Studio agents can invoke these capabilities mid-dialogue, without breaking context.

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The MCP Server for Azure AI Foundry (currently in experimental version) provides a unified integration layer between the different services of the Azure ecosystem:

• model exploration and comparison;
• knowledge base and embedding management;
• performance evaluation and monitoring via natural language.

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This convergence between MCP and Foundry illustrates a deep-seated movement: the transformation of large cloud environments into open agentic infrastructures, capable of interoperating through a common standard.

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By uniting frameworks like LangGraph, CrewAI, and Azure AI Foundry around the same protocol, MCP positions itself as the universal exchange language for distributed intelligence.

It becomes the foundation for a shared technical governance, where every agent, regardless of its execution environment, can dialogue with others according to transparent, auditable, and secure rules.

Differentiation: MCP vs RAG vs Function Calling

The Model Context Protocol (MCP) is not merely a technical alternative — it redefines how language models interact with their environment.

To measure its scope, it is essential to compare it to three commonly used approaches in the AI ecosystem: Retrieval-Augmented Generation (RAG), Proprietary Function Calling, and Workflow Automation Tools.

MCP vs RAG: From Static to Living Context

Retrieval-Augmented Generation (RAG) allows a model to enrich its responses by retrieving passages from a vector knowledge base.

These bases contain pre-embedded documents, meaning they are converted into numerical representations (vectors) before querying.

The model then queries this base via a similarity search to retrieve relevant excerpts.

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Limitation: This approach works well for static documentary corpora (reports, articles, manuals), but becomes inefficient when data changes frequently or needs to be retrieved dynamically.

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The Model Context Protocol, on the other hand, operates on a different logic.

Instead of querying fixed documents, MCP allows LLMs to access structured, real-time data directly, from databases, APIs, business tools, or SaaS services.

It requires no pre-embedding, and acts on demand (runtime) to fetch the correct information, at the right time.

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In Summary:

• RAG = useful for searching static, time-frozen corpora.
• MCP = essential for living, sensitive, or evolving data, requiring a direct and secure query.

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MCP thus transforms a model's contextualization from simple document retrieval into a dynamic and governed process.

MCP vs Proprietary Function Calling: From Dependence to Open Standard

Function calling introduced by OpenAI (and used in other forms in ChatGPT plugins) marked an important step in LLM connectivity.

They allow models to invoke pre-defined functions by the developer, but these approaches are proprietary: each vendor imposes its own format, its own rules, and its own connectors.

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Result: a strong dependence on the vendor's ecosystem.

A connector developed for GPT-4 is not compatible with Claude or Gemini, forcing companies to duplicate their efforts for each model.

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MCP addresses this problem at the root.

It defines a universal and open standard, based on documented conventions shared among vendors.

Developers can thus write once and execute everywhere, without waiting for a particular model to support a new feature.

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Furthermore, MCP easily integrates into proprietary or on-premises enterprise environments, allowing organizations to expose their own MCP servers so that models can utilize internal data, without depending on the public cloud.

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In Summary:

• Proprietary function calling = powerful, but locked into a closed ecosystem.
• MCP = open, interoperable, and sovereign, designed for durability.

MCP vs Workflow Automation: From Recipe to Protocol

Workflow automation tools like n8n or Zapier have democratized application integration via pre-defined connectors.

They allow the creation of "if this, then that" scenarios with little to no code, connecting dozens of SaaS tools.

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Their limitation: these platforms rely on specific integrations that constantly need updating. They are neither dynamically extensible nor designed to interact directly with language models.

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MCP, on the contrary, is an open and extensible protocol, progressively adopted by most LLMs and agent frameworks. It offers developers the ability to describe capabilities once, in a universal format, and execute them everywhere — whether on the cloud or on-premises.

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Furthermore, MCP bridges the gap between automation and reasoning: where Zapier executes linear actions, MCP allows an AI agent to plan, choose, and dynamically adapt its actions according to context and feedback received.

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In Summary:

• Workflow automation = static automation, application-centric.
• MCP = dynamic orchestration, centered on intelligent agents and living data.

Use Cases and Adoption

The Model Context Protocol (MCP) was designed to meet a specific need: to allow artificial intelligence agents to interact with the real digital world in a secure, dynamic, and interoperable manner.

Its advantages are particularly evident in environments where data evolves rapidly, governance is strict, and the complexity of flows exceeds the capabilities of a simple static API.

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Here are the most relevant application domains for the protocol:

1. Applications Requiring Real-Time Access to Living Data

MCP is ideal for systems where data constantly changes:

• BigQuery metrics updated by the minute,
• customer orders or inventory in PostgreSQL,
• support tickets in Slack, Zendesk, or Jira,
• or even activity logs and IoT streams.

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Thanks to its on-demand invocation capabilities (runtime), MCP allows AI agents to retrieve the most recent information, without pre-processing or re-training, while respecting security and performance constraints.

2. Secure Integration with Sensitive Enterprise Systems

MCP stands out for its native compatibility with modern security standards:

• OAuth 2.1 for authentication and delegation,
• RBAC (Role-Based Access Control) for permission management,
• and ephemeral tokens for granular access control.

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This makes it a perfectly suited solution for critical systems — ERP, CRM, financial platforms, private cloud environments — where data must be protected while remaining accessible to AI agents under human supervision.

3. Multi-Step Workflows and Inter-System Orchestration

Complex workflows involving multiple services, databases, or business applications require a protocol capable of coordinating actions in a reliable and traceable manner.

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MCP offers this capability through its client-server model with persistent sessions and contextual reasoning capacity.

An agent can thus:

• read a resource in a database,
• call a tool to transform the data,
• then trigger an action in another service,
  all within a single transactional cycle.

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This is the cornerstone of industrial multi-agent architectures, where multiple intelligences cooperate on distributed tasks.

4. Development of Autonomous Agents with High Reasoning

Agents that must not only execute orders but also reason, anticipate, and adapt according to the context particularly benefit from MCP.

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The protocol allows them to combine:

• access to structured and verified information sources,
• contextualized business prompts,
• and executable tools to act in the real world.

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This combination paves the way for truly autonomous agents, capable of understanding a global business objective and planning their actions across multiple interconnected systems.

5. Interoperability in Multi-Vendor Ecosystems

Finally, MCP addresses a major challenge for large enterprises: avoiding dependency on a single vendor.

Thanks to its open and standardized nature, the protocol allows models from different labs (OpenAI, Anthropic, Google, Mistral, etc.) to interact with the same tools and resources, without recoding integrations each time.

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This is a strategic lever for building multi-cloud and sovereign AI environments, where portability and compatibility are guaranteed by the protocol itself.

Pioneers Already Committed

Several technology companies have adopted MCP since its first version, including Block (Square), Apollo, Zed, Replit, Codeium, and Sourcegraph.

Their common goal: enable their AI agents to deeply understand the application context, interact intelligently with code and systems, and produce more relevant, nuanced, and functional results.

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In summary, MCP establishes itself as the universal integration infrastructure for modern AI—a bridge between the logic of models and the complexity of real systems, combining performance, security, and interoperability.

Conclusion

The Model Context Protocol (MCP) represents much more than a technical innovation—it is a major structural advance toward a more mature, composable, and interoperable artificial intelligence infrastructure.

By defining a common language between LLM applications and external systems, MCP transforms a still fragmented landscape into a coherent, governable, and extensible ecosystem.

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Thanks to its clear client-server architecture, its structured messages in JSON-RPC 2.0, its three powerful primitives (resources, prompts, tools), and its flexible transport mechanisms (STDIO, HTTP, SSE), MCP shifts the development of AI applications from a logic of custom and fragile integrations to a modular, standardized, and scalable approach.

Security, Oversight, and Sovereignty

One of the distinctive strengths of the protocol lies in its ethical and operational foundation.

MCP integrates human oversight, explicit consent, and multi-layer security as design principles.

Every interaction between agent and resource is auditable, controlled, and reversible, ensuring that the capabilities it unlocks remain under human governance.

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This model allows companies to harness the power of AI agents without sacrificing confidentiality, sovereignty, or traceability—three essential pillars for any responsible adoption of AI in critical environments.

A Rapidly Expanding Ecosystem

The adoption of the protocol is accelerating at an unprecedented pace.

With:

• hundreds of official third-party servers, covering major technical and business environments
• SDKs available in more than 10 languages (Python, TypeScript, Java, C#, Rust, etc.);
  an official registry (MCP Registry) facilitating server discovery and certification;
• and the massive support of major industry players—OpenAI, Google DeepMind, Microsoft, AWS,

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MCP is rapidly establishing itself as the de facto standard for context integration in agentic AI systems.

An Infrastructure for the Next Generation of Distributed Intelligences

By making models capable of interacting with live data, business tools, and enterprise systems in a standardized, secure, and governable manner, MCP opens the way to a new generation of AI: agentic, contextualized, and responsible.

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Its ambition is not to multiply models, but to connect them intelligently—to create an interoperable agent mesh capable of collaborating, learning, and acting in the service of human decision-making.

In this sense, MCP is to AI what TCP/IP was to the Internet: an invisible but essential infrastructure, allowing intelligences to communicate, cooperate, and evolve within a universal framework.