The Model Context Protocol (MCP) was born out of a simple yet decisive requirement in the AI ecosystem: to reliably and governably link language models to the real world. Introduced by Anthropic in November 2024 and published as an open standard, MCP proposes a common grammar so that LLMs can understand a goal, converse with tools (APIs, databases, business applications) and then perform traceable actions in an operational environment.
Beyond the technical novelty, MCP addresses a structural problem that was slowing the large‑scale adoption of agents in companies: the proliferation of ad‑hoc connectors and point‑to‑point integrations, which are costly to maintain and difficult to audit. By normalizing exchanges (message format, critical metadata, journaling) the protocol reduces the “N×M” effect between models and tools, accelerates integration and lays the foundations for durable interoperability – a necessary condition for moving from prototype to production.
Concretely, MCP creates an orchestration framework in which each interaction can be explained, verified and, if necessary, supervised by a human. This combination of openness (public standard), portability (independence from a single provider) and native auditability makes it a key building block for deploying multi‑agent architectures that are robust, compliant and scalable within organizations.
The isolation problem of LLMs
Despite spectacular advances in reasoning and conversation, large language models remain structurally disconnected from their operational environments. In practice they remain locked behind information silos and legacy systems: they see neither up‑to‑date business data, nor external APIs, nor execution tools (CRM, ERP, document management, cloud services) that would enable them to act in a useful and measurable way.
This technical claustration has a direct impact on the value created. A model only mobilizes what it learned during its training; it can neither query an up‑to‑date database, nor read a project folder, nor trigger an action (create a ticket, validate a batch, update a field) within a management system. The result: the answer may be brilliant linguistically but disconnected from the real context and unusable in an operational workflow.
Concretely, this translates into:
- pilots that stall at the demonstration stage because they are not integrated with everyday tools;
- decisions that aren’t traceable, because the model neither writes nor reads in the systems where evidence is stored;
- an inability to orchestrate chained tasks (reading a document repository, coherence check, writing a deliverable, updating a reference).
As long as LLMs remain confined to abstract question–and–answer logic, their potential is mechanically limited. Hence the need for a standardized interaction framework – such as MCP – to reconnect AI to the world of live data and enterprise tools with real traceability, governance and capacity for action.
The M×N combinatorial problem
Before the advent of the Model Context Protocol (MCP), every connection between an AI system and an external data source had to be developed from scratch. This artisanal approach, inherited from the first generations of AI integrations, led to extreme fragmentation of architectures and a genuine scaling nightmare.
To connect M AI models to N business tools or services required designing M × N distinct connectors, each with its own logic, dependencies, exchange formats and security constraints. This combinatorial explosion was not only complex to manage; it made industrialization practically impossible.
The consequences were many:
- Prohibitive development costs. Every integration required several weeks of specialized work, mobilizing rare and expensive skills. As the number of models and tools increased, costs grew exponentially.
- Chronic technical fragmentation. Connectors created for OpenAI were incompatible with those for Anthropic, themselves different from those for Google Gemini. Teams found themselves duplicating data schemas, pipelines and business logic, leading to a loss of efficiency and increased complexity.
- Massive technical debt. Maintaining a network of ad‑hoc integrations quickly became a burden: every upstream API update triggered a cascade of fixes in connected systems, slowing deployment cycles and undermining overall stability.
- Confusion about roles and responsibilities. AI teams had to simultaneously master the business logic, the peculiarities of the models and the technical specifics of each API. This lack of clear separation of responsibilities diluted their expertise, created organizational bottlenecks and limited the speed of innovation.
In short, before MCP every attempt at AI integration was a sophisticated yet fragile patch‑work. Companies built temporary bridges between their models and their tools, without standards or governance, at the cost of growing complexity and almost impossible scalability. MCP was designed precisely to break with this logic, by establishing a common language between models, tools and digital environments.
Standardization inspired by the Language Server Protocol
Faced with this growing complexity, the Model Context Protocol (MCP) introduces a universal architecture. Instead of an explosive interconnection problem of M × N, MCP reduces it to a much more manageable equation: M + N. In other words, each AI model and each business tool no longer need to talk directly to each other – they now communicate through a common language.
The inspiration for this protocol comes from the world of software development, and more specifically from the Language Server Protocol (LSP) created by Microsoft to standardize exchanges between code editors (VS Code, Vim, JetBrains …) and syntax analyzers. Before LSP, each editor had to implement specific support for each language – a situation comparable to that of the AI ecosystem before MCP.
MCP applies this same principle of unification to artificial intelligence. Where LSP simplified life for developers, MCP standardizes communication between AI models and external resources (APIs, databases, business tools, cloud services). In this model:
- each tool or data source implements a single MCP server capable of responding to standardized requests;
- each application or AI agent integrates a single MCP client capable of understanding and exploiting these responses;
- the whole forms an interoperable ecosystem in which components can be replaced, combined or extended without complete redevelopment.
By unifying exchanges between artificial intelligences and digital systems, MCP abolishes the barriers between proprietary environments. It transforms integration – previously a technical headache – into a standardized, traceable and lasting dialogue, opening the way to a genuine intelligent mesh between models and tools.
Problems solved by MCP
Interoperability
The Model Context Protocol (MCP) is based on a clear philosophy: openness and neutrality. Designed as an open and agnostic protocol, it works with any language model – whether it’s Claude, GPT‑4, Gemini, Mistral or any other compatible LLM – and can connect to any data source or external tool, without technological dependence.
In other words, MCP does for the AI ecosystem what HTTP did for the Web: it provides a universal dialogue layer between heterogeneous entities. Gone are closed architectures and fragile bridges between competing solutions: a single AI agent can now query a PostgreSQL database, consult a GitHub repository or execute a cloud command, all via a common, documented and interoperable protocol.
Technically, MCP is built on the proven JSON‑RPC 2.0 standard, which it enriches with a set of structured metadata – context, source, date, priority, status, justification and confidence level. This approach ensures clear, verifiable and traceable communication between the various actors in the system (agents, tools, databases, services).
Thanks to this foundation, exchanges become not only interoperable but also auditable and durable. MCP doesn’t just connect artificial intelligences to their environments: it gives them a common language that is stable and transparent, capable of crossing technological boundaries and supporting the rise of agentic AI at enterprise scale.
API fragmentation
Before the appearance of the Model Context Protocol (MCP), developers had to navigate a fragmented and heterogeneous ecosystem.
Depending on use cases, they resorted to OpenAI’s function calling, ChatGPT plugins, frameworks like LangChain, or a multitude of custom‑developed REST or GraphQL APIs. Each of these approaches imposed its own conventions:
- a different schema format for data;
- a specific orchestration logic;
- and an independent maintenance cycle, often heavy and fragile.
This technological dispersion led to strong dependency on providers and a lack of overall coherence: integrations were effective locally but incompatible with each other and difficult to industrialize.
MCP puts an end to this complexity by centralizing interactions within a standardized client–server architecture built on simple, universal principles:
- MCP servers expose tools, resources and prompts according to standardized schemas. Each server acts as a structured catalog of capabilities accessible to agents.
- MCP clients, integrated into models or agents, maintain stateful sessions, allowing them to retain a persistent context from one interaction to the next – a decisive advance for continuous reasoning and long‑term supervision.
- MCP hosts (such as Claude Desktop, VS Code or Cursor) orchestrate connections between servers and clients. They apply security policies, manage permissions and ensure that every exchange remains traceable, controlled and compliant.
Thanks to this unified model, MCP not only harmonizes communication between artificial intelligences and their tools: it creates a stable, interoperable and governable infrastructure in which every component – model, application or resource – naturally fits into a coherent ecosystem.
Vendor lock‑in
One of the main criticisms leveled at proprietary approaches – such as OpenAI’s function calling or ChatGPT plugins – concerned their ecosystem lock‑in. A tool or connector designed for GPT‑4 was not compatible with Claude or Gemini, forcing companies to make an exclusive provider choice or duplicate their developments for each environment. This structural dependency limited interoperability and slowed the spread of use cases on a large scale.
The Model Context Protocol (MCP) was specifically designed to break with this closed model. As an open standard published under the MIT license, it can be implemented freely by any market player, regardless of the language model or platform. Its objective is clear: to ensure total interoperability and durable portability of tools across different AI ecosystems.
The year 2025 marks a decisive turning point. After its introduction by Anthropic, the protocol was adopted by OpenAI (March 2025) and then by Google DeepMind (April 2025), confirming the emergence of an industry consensus around a neutral, shared framework. For the first time, the major players in the sector are converging on a common protocol, not controlled by a single actor, capable of serving as the foundation for real interoperability.
However, this momentum of openness remains fragile. As long as Anthropic remains the main maintainer of the protocol without multi‑party governance (such as W3C, ISO or an OpenAI Alliance) being established, there is a risk of fragmentation or unilateral control. The durability of MCP will therefore depend on its ability to be structured as a common good, governed collectively, guaranteeing its independence from the commercial strategies of its founders.
Data sovereignty
One of the major strengths of the Model Context Protocol (MCP) lies in its ability to offer companies granular control over access to and flow of data. Where centralized cloud APIs often require transferring information to remote servers – raising issues of compliance, security and sovereignty – MCP introduces a radically different approach: data stays where it is.
Thanks to its flexible architecture, the protocol allows MCP servers to operate both locally (stdio) and through HTTP connections using Server‑Sent Events (SSE). This flexibility gives organizations the option of choosing their deployment mode according to their security and governance constraints:
- Deploy MCP servers within their own infrastructures, whether on‑premises or private cloud, to meet data residency requirements, notably those linked to GDPR and industry‑specific regulations.
- Implement modern authorization mechanisms, compatible with OAuth 2.1 standards and decentralized identifier (DID) authentication, reinforcing control over identity and permissions.
- Accurately audit access flows, tracing what data are exposed, to which agents, and under what conditions, thanks to task‑based access control (TBAC) policies.
This architecture ensures complete traceability and fine‑grained control of confidentiality, without compromising operational efficiency. MCP thus reconciles interoperability and governance, allowing companies to harness the power of AI agents while maintaining control over their sensitive data – an essential condition for deploying agentic systems in regulated or critical environments.
Comparison with earlier approaches
MCP vs OpenAI Function Calling
The function calling introduced by OpenAI in 2023 represented a first attempt to give language models a structured ability to act. The principle was simple: the model could invoke predefined functions by generating JSON objects conforming to a given schema. This innovation kicked off interaction between LLMs and external systems, paving the way for concrete use cases (data retrieval, automation, command execution).
But this approach, while effective at small scale, suffers from structural limitations that hinder its adoption in complex environments:
- Proprietary coupling. Function definitions and their metadata are tightly linked to the OpenAI ecosystem, making integrations difficult to reuse in other contexts (Claude, Gemini, Mistral, etc.).
- Lack of persistence. Each function call is stateless, i.e. independent of the previous one. The model does not retain the logical thread of a session or the memory of intermediate states.
- Static discovery. Functions must be declared in advance before launching the session. It is therefore impossible to add new tools or resources dynamically during execution.
- Limited scalability. Orchestration rests on the developer, who must manually manage the sequence of calls, synchronization and context management – a task that quickly becomes unmanageable at scale.
The Model Context Protocol (MCP) overcomes these constraints by proposing a session‑based and dynamic architecture:
- MCP agents operate within stateful sessions, maintaining a continuous context between exchanges.
- The available tools can be discovered dynamically at startup, without prior configuration.
- The orchestration is standardized, the protocol supports parallel invocation of multiple functions and smooth coordination between several agents or services.
In summary, MCP transforms the paradigm initiated by OpenAI’s function calling: it is no longer a question of occasionally calling a function, but of orchestrating a fluid, traceable and governed conversation between intelligences and systems, within an interoperable and lasting framework.
MCP vs LangChain
LangChain has established itself as one of the reference frameworks for creating AI agents. It offers a complete toolbox for building complex chains of reasoning, integrating multiple language models and exploiting retrieval‑augmented generation (RAG). Thanks to patterns such as ReAct (Reason + Act), LangChain makes it possible to design agents capable of interacting with their environment iteratively, combining reasoning and execution.
However, this functional richness comes with an additional layer of abstraction, which can make execution heavier and debugging more complex. LangChain acts as a meta‑framework on top of LLMs, orchestrating calls and intermediate memories, whereas protocols like MCP or OpenAI’s function calling operate more directly at the level of exchanges between model and tool.
Key differences includes
- Speed. Function calling – and by extension MCP more quickly than LangChain textual agents, because they remove the intermediate layer of linguistic interpretation and limit successive calls.
- Transparency. LangChain advocates an explicit approach to reasoning: every thought step, every agent decision can be followed and analyzed. MCP, by contrast, treats tool invocation as a capsulated transaction – a “black box” – privileging standardization and performance over readability of reasoning.
- Flexibility. LangChain excels at designing complex and adaptive workflows involving multiple steps of reflection, verification or content generation. MCP adopts the opposite philosophy: reduce complexity by normalizing exchanges to ensure coherence and portability on a large scale.
In reality, these two approaches are not opposed: they can be highly complementary. It is entirely possible to use a LangChain agent to drive complex reasoning chains while invoking tools via MCP. This combination associates the logical control and modularity of the LangChain framework with the interoperability and standardization of the MCP protocol, offering an ideal balance between agility, governance and performance.
MCP vs REST/GraphQL
The REST and GraphQL protocols have been the pillars of the modern web for more than a decade. Designed for stateless exchanges between clients and servers, they have standardized communication between human applications and digital services. Their effectiveness for classic operations – creating, reading, updating and deleting data (CRUD) – is indisputable. However, these paradigms reach their limits in the face of the needs of AI agents, which require continuous, contextual and governed exchanges.
Architectural dimensions:
While REST and GraphQL aim to facilitate single and predictable exchanges between human applications and remote services, the Model Context Protocol (MCP) adopts a different logic, suited to the agentic ecosystem.
MCP does not seek to replace REST or GraphQL; it distinguishes itself by its purpose. Where these protocols orchestrate unitary transactions, MCP manages persistent sessions between AI agents and external resources, capable of maintaining a shared and evolving context.
This approach introduces several breakthroughs.
- Dynamic discovery of available tools during the initialization phase, with no need for prior static configuration.
- A persistent context that preserves the history of exchanges and decisions, enabling consistent and coherent reasoning.
- Native bidirectional events, thanks to the use of Server‑Sent Events (SSE), which allow systems to notify agents in real time – a key element for supervision and multi‑agent collaboration.
In short, MCP doesn’t replace existing API paradigms: it completes and extends them. It positions itself as the communication layer of the agentic world, the one that allows artificial intelligences to converse with each other and with their digital environments in a standardized, traceable and evolving framework.
Context of emergence and adoption
Adoption timeline
The adoption of the Model Context Protocol (MCP) has been exceptionally rapid, illustrating the willingness of major players in artificial intelligence to converge on a common standard. In less than a year, MCP has gone from an experimental project to an industry benchmark for interoperability between models and tools.
- November 2024 – Launch by Anthropic.
Anthropic announces the creation of the Model Context Protocol as an open standard, accompanied by SDKs in TypeScript and Python. The stated goal: to enable language models to reliably converse with external tools and data, whatever the provider. - End 2024 – First industrial partners.
Technology companies such as Block (Square), Apollo, Replit, Codeium and Sourcegraph quickly integrate MCP into their platforms. These pioneers demonstrate the protocol’s value for development, documentation and software productivity use cases. - March 2025 – Adoption by OpenAI.
OpenAI’s announcement of the official adoption of MCP marks a strategic turning point: for the first time the two main competitors in the market, Anthropic and OpenAI, align on the same technical base, laying the foundations for an interoperable standard across the sector. - April 2025 – Entry of Google DeepMind.
Through Demis Hassabis, Google DeepMind confirms integration of MCP into Gemini and its SDKs, hailing “a good protocol that is quickly becoming an open standard for the agentic era of AI.” This recognition institutionalizes MCP as a reference infrastructure for coordinating intelligent agents. - April 2025 – Alliance with Microsoft.
Microsoft partners with Anthropic to co‑develop an official C# SDK, designed to facilitate MCP integration into the .NET environment as well as into Copilot Studio, VS Code and Semantic Kernel. This partnership reinforces the protocol’s anchoring in professional development tools. - May 2025 – AWS engagement.
Amazon Web Services joins the MCP steering committee and launches Strands Agents, an open source SDK compatible with MCP and other emerging standards. This initiative confirms AWS’s desire to participate in an open, multi‑cloud interoperability ecosystem. - October 2025 – Launch of the MCP Registry by GitHub.
GitHub inaugurates the MCP Registry, a centralized hub allowing developers to discover, install and manage MCP servers. More than 40 official servers feature at launch, offered by Microsoft, GitHub, Dynatrace, Terraform and the open source community.
In less than twelve months, MCP has thus gone from an experimental protocol to an industrial standard. Its adoption trajectory reflects a profound shift in the sector: entry into an open agentic era, where collaboration between AI, tools and infrastructures finally rests on a common language.
Ecosystem and network effects
Exponential growth of the MCP ecosystem
From February 2025, the ecosystem surrounding the Model Context Protocol (MCP) began to grow at a blistering pace: over 1 000 MCP servers had already been created by the open source community. Just eight months later, in October 2025, this number continued to increase exponentially, supported by powerful network effects comparable to those seen during the standardization of the Web or the HTTP protocol.
Two major network effects are at work:
- Direct network effect. Every new MCP server – whether it’s an integration with Slack, GitHub, Salesforce or an internal database – instantly increases the value of the entire ecosystem. Any compatible MCP client can access these new tools without code modification, creating a cumulative effect of interoperability that accelerates the protocol’s spread.
- Indirect network effect. The adoption of the protocol by the major industrial players such as Google, Microsoft, OpenAI and AWS acts as a powerful signal of trust. This legitimacy in turn attracts more independent developers, start‑ups and companies, enriching the diversity of servers and multiplying use cases.
Towards a real MCP economy
A new creator economy is taking shape around the protocol. Developers are designing and monetizing premium MCP servers, offering advanced features (enhanced security, analytics, vertical integrations) available through marketplaces, SaaS subscriptions or sponsorship programs.
Specialized platforms, such as Smithery.ai, are gradually emerging as distribution and certification hubs for MCP connectors. They play the role of trusted third parties, ensuring the quality, compatibility and security of servers offered to the community.
In less than a year MCP has thus gone from a technical protocol to an economic engine. Its adoption is no longer driven solely by engineering logic but by an open market dynamic, where interoperability rhymes with shared innovation and collective value creation.
Economic and strategic issues
Reduced of integrations costs
Economically, the Model Context Protocol (MCP) brings about a structural transformation of the cost model for integrations. Where companies once faced a quadratic problem – with M × N integrations to maintain between AI models and business tools – MCP reduces this complexity to a linear problem: M + N integrations now suffice. Each model and each tool only need to implement a single MCP connector to become compatible with the entire ecosystem.
The direct benefits of this standardization are substantial:
- Drastic reduction in development time.
Integrations that required several weeks of specialized work can now be completed in a few hours, thanks to unified SDKs and standardized schemas. - Significant reduction in technical debt.
By eliminating the multiplication of proprietary connectors, MCP simplifies maintenance and reduces the risk of regressions with each API update. Teams gain in stability and predictability. - More intelligent allocation of resources.
Freed from repetitive integration tasks, development teams can focus on what really creates value: the business logic, the quality of models and the user experience.
By reducing redundant efforts and lowering maintenance costs, MCP doesn’t just improve technical productivity: it redefines the economics of integration in AI environments, making it scalable, predictable and sustainable.
Standardization and open ecosystems
The Model Context Protocol (MCP) fully embodies the logic of a digital public good. As an open standard, it generates positive externalities that benefit the entire artificial intelligence ecosystem: pooling development efforts, increased interoperability between tools and the emergence of a common language between agents and infrastructures
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This philosophy stands in contrast to the proprietary strategies adopted by some historical players, notably OpenAI, which keeps part of its technologies under closed license. By promoting a collaborative approach, Anthropic positions itself as an ecosystem architect rather than as a simple model provider – a posture that appeals both to the open‑source developer community and to large groups seeking technological sovereignty.
Persistent tensions
But this openness is not without from dark zones :
- Lack of neutral governance.
MCP is not yet administered by an international standards body (like W3C or ISO) but remains under the direct responsibility of Anthropic. This dependency raises the question of the protocol’s long‑term sustainability and the transparency of technical decisions.
- Risk of fragmentation.
If Anthropic were to impose certain evolutions unilaterally, other actors could react by launching their own competing protocols – similar to the A2A (Agent-to-Agent Protocol) developed by Google – thereby recreating the initial fragmentation that MCP was precisely intended to eliminate. - Competition between standards.
MCP is not alone in the field: it coexists with other initiatives such as the Agent Communication Protocol (ACP), A2A and the Agent Network Protocol (ANP). Each tackles a complementary dimension of agentic interoperability (communication, coordination, governance) but this plurality of standards could ultimately divide adoption efforts.
Implications for businesses
The adoption of MCP is not just a technical evolution – it is a strategic bifurcation. It reshapes the way companies design, deploy and govern their artificial intelligence systems.
- Accelerating agentic AI.
MCP makes it possible to move from static AI, limited to its training corpus, to dynamic and contextual AI, capable of interacting in real time with business data, internal documents or production systems. - Reduction of vendor lock-in.
Thanks to its open architecture, MCP gives companies the freedom to switch models – move from GPT‑4 to Claude, or from Claude to Gemini – without having to rewrite all their integrations. It becomes a universal abstraction between models and systems. - Enhanced governance and security.
By opening access channels between AI and tools, MCP introduces new security risks: supply‑chain attacks, prompt injection or malicious servers. Companies must therefore implement strict governance policies, including internal server registries, whitelists and sandboxing mechanisms to limit the exposure surface. - Interoperability by 2027.
According to a forward‑looking study by Gartner, by 2027 more than a third of agentic AI implementations will combine several agents with complementary skills, collaborating through protocols such as MCP. This evolution heralds the emergence of a true mesh of interoperable intelligences, capable of permanently transforming the structure of information systems.
Prospects and challenges
Towards multi-actor governance?
As the Model Context Protocol (MCP) becomes the de facto standard, the question of its governance becomes central. To avoid unilateral control by a single actor, several voices – from the open‑source community, major cloud vendors and regulators – are calling for the creation of a multi‑company consortium to steer the evolution of the protocol.
Such an organism could play a decisive role in the sustainability and legitimacy of the standard, in:
- Establishing ethical and security frameworks, to ensure that the protocol remains aligned with principles of transparency, sovereignty and data protection.
- Coordinating technical evolutions through community working groups where industry players, researchers and open‑source developers would participate in defining extensions and fixes.
- Ensuring the transparency in the standardization process, like the W3C for the Web, in order to limit the risks of fragmentation and ensure interoperability between implementations.
A governance that is open and distributed would thus be the guarantor of a sustainable ecosystem, in which MCP remains a technological common good, and not the strategic instrument of a single company.
Security and trust challenges
The rapid adoption of MCP within companies nevertheless introduces a series of critical risks that must be anticipated and addressed rigorously.
- Insufficient authorization.
Early versions of the protocol (before March 2025) lacked native permission management mechanisms. Some servers deployed in production still do not incorporate robust authentication, leaving the door open to unauthorized access. - Expanded attack surface.
Each new MCP server added to an environment increases the potential surface area exposed to vulnerabilities. Without centralized control, the risk of compromise or data leakage grows proportionally with the size of the network. - Malicious servers.
The ease of deploying an MCP server cuts both ways: it fosters innovation but also allows the introduction of “shadow” servers lacking security, installed without official validation. - Prompt injection.
Malicious prompts can exploit the logic of the protocol to trigger data deletions, bypass access rules or cause leaks of sensitive information.
Mitigation best practices
To secure their deployments, organizations need to adopt a strict governance hygiene :
- Systematic verification of digital signatures servers;
- Sandboxing execution environments to isolate agents;
- Code review and internal validation before production deployments;
- Internal registries of approved servers, with updated whitelists;
- Continuous auditing agent logs and behaviors, to detect any drift or anomaly.
The security of MCP therefore does not depend solely on the protocol itself, but on the operational maturity of those who deploy it. It is by combining an open standard, collective governance and cybersecurity discipline that the ecosystem can earn the lasting trust of companies and institutions.
Conclusion: MCP as the infrastructure of agentic AI
The Model Context Protocol (MCP) is not just a technological evolution – it is a foundational infrastructure for the new era of agentic artificial intelligence. By providing a clear answer to the problems of model isolation, ecosystem fragmentation and combinatorial integration complexity, MCP unlocks the true potential of AI agents: to interact with the real world in a standardized, secure and scalable way.
In less than a year, the protocol has moved from concept to industrial adoption. Its rapid integration by Google, OpenAI, Microsoft and AWS, coupled with the exponential growth of its open‑source ecosystem, signals an unprecedented convergence in a historically fragmented sector. This dynamic is not trivial: it marks the birth of a common language for collaboration between artificial intelligences, digital tools and business systems.
But for MCP to become the universal protocol of agentic AI over the long term, several challenges remain:
- Truly multi‑actor governance, ensuring the neutrality of the standard;
- End‑to‑end security, in the face of rising risks of exploitation and misuse;
- and strengthened interoperability with other emerging protocols such as A2A, ACP or ANP, to avoid re‑fragmentation of infrastructures.
From this perspective, MCP follows in the footsteps of major standards that have shaped digital history: HTTP/TCP‑IP for the Internet, USB‑C for hardware or HTML for the Web. It is not a product, but a digital public good – an invisible yet essential layer on which the distributed intelligence of tomorrow will be built.
If governed with transparency, rigor and a spirit of cooperation, the Model Context Protocol could become much more than a tool for integration: the technical foundation for collective innovation, open, sustainable and truly serving humanity.