Launched open source by Anthropic in November 2024, the Model Context Protocol (MCP) represents more than just technical progress: it is a strategic turning point in the architecture of artificial intelligence. In less than a year, the protocol has experienced rapid adoption: over 1,000 MCP servers developed by February 2025, and an ecosystem exceeding 5,500 servers by autumn.
This spectacular growth is not just a community fad. It reflects a structural shift: the transition from a human-user-centric tool-AI to an agent-AI embedded in distributed ecosystems.
Until now, language models operated in siloed environments—responding to specific, one-off queries, without real memory or capacity for action beyond their dialogue space. With MCP, these models acquire an interoperation capability: they can now act in the digital world, interface with external systems, orchestrate data flows, and collaborate with other agents.
This evolution paves the way for an agentic web, where interactions are no longer limited to human-machine interfaces but are deployed between autonomous agents capable of reasoning, learning, and cooperating. The protocol thus becomes the common language of this new infrastructure layer, connecting AIs to each other as TCP/IP once connected computers.
The MCP inaugurates a cognitive interconnection infrastructure: each server exposes capabilities, each agent discovers and uses them, and each interaction is traceable, verifiable, and governed. This approach lays the foundation for distributed intelligence, not centralized in a single model, but spread across cooperative entities sharing standard protocols.
In this perspective, MCP plays the same role for the agentic era as HTTP for the web or Kubernetes for the cloud: an invisible yet structuring standard that enables large-scale coordination.
Anticipating the future of the MCP requires recognizing the systemic scope of this innovation. The protocol does not just transform how AIs are integrated; it reconfigures:
- Economic models, by fostering the creation of interoperable ecosystems and agent marketplaces.
- Technological power balances, by redefining dependencies between cloud, data, and AI players.
- Ethical and regulatory frameworks, by raising the question of governance for decisions made by autonomous agents.
- Digital sovereignties, because an open protocol controlled by a few actors can become a major geopolitical lever.
The MCP is part of the trajectory of major digital coordination infrastructures: those that, by establishing themselves as de facto standards, shape the global economy for decades. Its rise announces a future where AIs will no longer be isolated tools but intelligent partners embedded in fluid, governable, and potentially planetary networks.
Protocol Evolution Perspectives: Modularity and Cross-Vendor Interoperability
Extensions, Plug-ins, and Standard Evolution
The Model Context Protocol (MCP) is currently based on a client-server architecture based on JSON-RPC 2.0, directly inspired by the Language Server Protocol (LSP).
This modular design is a deliberate choice: it guarantees the protocol native extensibility, meaning the ability to evolve without breaking compatibility.
Each new functional building block can be integrated as an extension, without challenging the core of the protocol—an essential approach for a standard meant to last.
During the September 2025 MCP summit, Anthropic and its partners presented an ambitious roadmap for 2025–2026, articulated around four priority evolution areas.
1. Multimodality and Streaming: Towards Perceptive Agents
One of the most anticipated projects concerns the native support for multimodality.
The objective: to allow agents to process text, audio, video, and visual data simultaneously, while maintaining contextual coherence between these streams.
This evolution relies on two key innovations:
- Native bidirectional streaming, for continuous and reactive exchanges between agents and servers.
- Dynamic chunking, enabling the segmentation of large data volumes (e.g., videos, time series, complex logs) to facilitate incremental processing.
With these capabilities, the MCP will become a true cognitive channel for agents capable of understanding a meeting, analyzing visual signals, or synchronizing events in real time—a decisive step towards AI embedded in operational environments.
2. MCP Registry: The Backbone of Trust
Launched in preview version in September 2025, the MCP Registry aims to become the "single source of truth" for the discovery, verification, and distribution of MCP servers.
This centralized registry brings several major innovations:
- Automated discovery: each agent will be able to identify compatible servers without manual configuration.
- Certification and signing: a trust verification mechanism authenticates servers via public keys, guaranteeing their integrity and origin.
- Meta-indexing: the registry does not store the code itself, but the deployment metadata (version, dependencies, maintainers, compatibility).
The goal is to provide companies with a secure, traceable, and governable ecosystem, avoiding the proliferation of unverified servers—an essential step for the industrial maturity of the protocol.
3. Enhanced Authentication and Authorization
One of the main challenges of the MCP remains the security of access flows between agents, clients, and servers.
The roadmap includes the full integration of modern security standards:
- OAuth 2.1 for secure authorization,
- Dynamic Client Registration (DCR) for automated registration of client applications,
- Enterprise SSO (Single Sign-On) via integrations with Okta, Azure AD, or Auth0,
- And the explicit separation between resource servers and authorization servers, to reduce the risks of privilege escalation.
Although these developments are already underway in the specification published in June 2025, challenges persist around compliance with the security policies of large enterprises—particularly for decentralized identity management and fine-grained permission delegation (hybrid RBAC/ABAC).
4. Reference Implementations and Multi-Language Interoperability
The success of the MCP relies on its ecosystem neutrality. Following the Python and TypeScript SDKs, implementations in Java, Go, Rust, and C# are now in development. Each is accompanied by automated compliance test suites, guaranteeing that servers and clients strictly adhere to the specification.
These tests, published as an "MCP Compliance Kit," will allow companies to validate their internal implementations before deployment—a prerequisite to prevent standard fragmentation.
Open Governance and Improvement Process
To ensure its sustainable evolution, the MCP relies on an ** open community governance model**. The core of this model is the Specification Enhancement Proposal (SEP)—a process inspired by PEPs (Python Enhancement Proposals) and IETF RFCs.
A proposal's life cycle follows six transparent steps:
Proposal → Draft → Provisional → Prototyping → In Review → Accepted.
Each SEP must be sponsored by a core maintainer and validated collectively after public discussion. Working Groups (security, multimodality, compliance, transport) and Interest Groups (user companies, academia, open source) meet regularly to guide decisions.
Bi-weekly meetings of core maintainers and the systematic publication of minutes ensure a high level of transparency.
This distributed model has a dual objective:
- Avoid the capture of the protocol by a dominant actor,
- And guarantee technical coherence in a rapidly expanding ecosystem.
MCP as a Building Block for an Agentic Web: Cooperative AIs and Interoperable Mesh
The Emergence of the Agentic Mesh
The concept of the Agentic Mesh extends the Model Context Protocol (MCP) towards a distributed architecture where several specialized agents collaborate within a coordinated network. The idea is inspired by service meshes (Istio, Linkerd) in the cloud world but transposes their supervision and orchestration logic to autonomous cognitive systems.
The Agentic Mesh is not just a technical evolution of the MCP: it embodies its maturity, where AI no longer just responds, but orchestrates, coordinates, and learns collectively.
The Founding Principles of the Agentic Mesh
- Composability and Modularity
Each agent, model, or tool becomes an interchangeable node of the network.
It can be added, replaced, or updated without modifying the other components, thanks to interface standardization via MCP.
This modularity offers a dual advantage: agility of evolution (incremental addition of capabilities) and scalability (natural horizontal scalability). - Distributed Parallel Reasoning
Where early AI architectures centralized thought in a single monolithic model, the mesh distributes tasks among specialized agents working in parallel.
An agent can, for example, extract data while another synthesizes it, and a third checks for consistency.
This asynchronous operation improves performance and allows for domain specialization: finance, health, logistics, scientific research... - Logical Decoupling and Layered Governance
The mesh separates responsibilities between logic, memory, orchestration, and interface.
Each agent operates autonomously while sharing a synchronized common context.
The whole is documented in a behavior logging system, which journals every tool invocation, every error, and every decision—an essential approach for traceability and compliance. - Vendor Neutrality
True to the open standard spirit, the Agentic Mesh favors open protocols (MCP, Google's A2A) over proprietary APIs.
Components can thus be replaced without global reconfiguration, ensuring interoperability and sovereignty.
Concrete implementations like AgentMesh or multi-server MCP architectures already demonstrate the viability of this model.
Interoperability and Competing Protocols
The MCP is currently the most advanced standard for agent-to-tool communication, but it is not alone in the field. Several competing or complementary initiatives seek to cover other layers of the agentic ecosystem.
1. Google Agent-to-Agent (A2A)
Announced in April 2025 with the support of over 50 partners (Salesforce, MongoDB, PayPal, etc.), the A2A protocol focuses on inter-agent communication rather than tool access. It also uses JSON-RPC 2.0 over HTTPS and introduces an agent card mechanism enabling the discovery of capabilities in a decentralized manner.
Google presents A2A as complementary to MCP:
- MCP manages the connection between agents and resources (data, APIs, tools).
- A2A manages the coordination between autonomous agents.
In practice, the two overlap in certain use cases, creating a healthy tension between convergence and specialization.
2. Other Emerging Protocols
- Agentica (WrtnLabs): aims to reduce integration costs by 50% by relying on lightweight models and simplified servers.
- Universal Tool Calling Protocol (UTCP): removes the proxy architecture to reduce latency by half, at the cost of losing flexibility.
These initiatives reflect a classic dialectic in the history of standards: MCP favors universal interoperability and robustness, while its alternatives explore raw performance or implementation simplicity.
3. Towards a Multi-Protocol Consensus
In the long term, the ecosystem could stabilize around a multi-layer architecture:
- MCP for data and tool access,
- A2A for inter-agent coordination,
- And specialized protocols (UTCP, Agentica) for vertical or embedded uses.
This convergence would recall the evolution of the Internet, where HTTP, FTP, and SMTP coexisted while fulfilling distinct functions.
The adoption by Microsoft of native MCP support in Windows 11, and its integration into GitHub Copilot and Copilot Studio, suggests that a common foundation is emerging, upon which sectoral extensions will be grafted.
Web of Agents: A Federated Architecture
The Web of Agents project (arXiv:2505.21550) formalizes this vision of an interoperable agentic ecosystem at the web scale. Its minimal architecture rests on four pillars:
- Agent-to-Agent messaging—standards for communication between autonomous entities.
- Interaction interoperability—standardized data formats and exchange protocols.
- Distributed state management—synchronization mechanisms to maintain coherence between agents.
- Agent discovery—open registries allowing the identification and qualification of available capabilities.
This work joins the initiatives of the W3C "Autonomous Agents on the Web" Community Group, which explores how to adapt web standards (HTTP, WebSockets, RDF, DID) to the needs of distributed intelligence.
The common objective: to avoid fragmentation into incompatible silos and lay the foundation for an "Internet of Agents"—an open, secure, auditable, and evolutive network, where humans and AI cooperate on an equal protocol footing.
Regulatory, Ethical, and Technological Sovereignty Challenges
Distributed Governance and Responsibility
The increasing autonomy of AI agents, made possible by the Model Context Protocol (MCP), disrupts traditional frameworks of responsibility and governance.
As soon as several agents collaborate within the same system—for example, a financial agent, an HR agent, and a logistics agent coordinating an operation—a central question arises: who is responsible in case of error, prejudice, or non-compliant decision?
The MCP, as a communication infrastructure, does not intrinsically define responsibility. It provides the channel, not the ethical or legal framework. This structural gap places governance at the heart of the agentic era's challenges.
Legal Responsibility and Regulatory Compliance
New regulations, such as the EU AI Act (effective August 2024), impose strict requirements on high-risk AI systems regarding transparency, traceability, and risk management.
To be compliant, an agentic architecture based on MCP must:
- Trace all inter-agent interactions thanks to detailed audit logging,
- Allow access revocation to a compromised agent or server,
- And make decisions explainable within a distributed reasoning process.
Current MCP specifications only partially integrate these functions.
The Compliance Working Groups are now working on a major project: adding compliance by design modules, allowing the direct integration of audit, access, and traceability policies into the protocol itself.
The logic of “governance by design” is thus becoming essential. Access, consent, and privacy policies must be encoded in the MCP configurations, not added a posteriori.
New approaches are emerging, such as Explainable MCP, which aims to record not only the executed actions but also the agents' underlying reasoning—an equivalent of the "intention log" within the agentic network.
Bias, Fairness, and Transparency
Like any technology based on learning models, systems based on MCP can amplify biases present in training data or in the tools they use.
An HR agent connected to a biased recruitment system could reproduce—or even extend—large-scale discrimination.
To address this, MCP actors are exploring several levers:
- Systematic bias audits, carried out at regular intervals on servers and agents,
- Integrated Fairness checks in MCP workflows, validating equity criteria before execution,
- Transparent decision chains, where each reasoning step is logged with its contextual metadata (source, date, justification).
The MCP Registry could play a structuring role by certifying ethical servers—i.e., those compliant with non-discrimination, privacy, and regulatory compliance criteria. Eventually, a “Fair MCP” label could be imagined, attesting to the ethical compliance of a server, similar to ISO or SOC certifications in the cloud.
Technological Sovereignty and Strategic Independence
Beyond ethical issues, the challenge is also geopolitical.
Europe has made digital sovereignty the core of its AI strategy, notably through two initiatives:
- the AI Continent Action Plan (April 2025),
- and the Apply AI Strategy (October 2025).
These plans aim to reduce dependence on American (OpenAI, Microsoft, Google) and Chinese ecosystems by developing an autonomous AI value chain: data, computing, models, and governance.
The MCP, although born at Anthropic (an American startup), paradoxically offers a unique opportunity for Europe. As an open standard, it is not captive to a single actor and can serve as the foundation for a sovereign agentic ecosystem.
Concretely, Europe could:
- develop public MCP servers for health, education, administration,
- build certified European registries, guaranteeing ethical and regulatory compliance,
- and encourage auditable open source implementations, hosted on sovereign infrastructures.
The “AI First” strategy promoted by the European Commission already encourages companies to integrate AI as a lever for competitiveness and productivity.
The MCP, by facilitating the integration of AI agents into existing business systems, could become the natural accelerator of this transformation—provided that European actors master the entire technological chain: models, computing infrastructures, and governance protocols.
Role of Open Source and Industrial Alliances
Community Dynamics and Industrial Adoption
The Model Context Protocol (MCP) did not just benefit from good technical design; it primarily found its strength in a strategy of radical openness. From its launch, Anthropic made the decisive choice to make the protocol open source, allowing any actor—individual developer, startup, or large group—to contribute without prior authorization. This decision triggered a distributed innovation dynamic that quickly transformed into a genuine ecosystem movement.
In less than a year, over 5,500 MCP servers have been developed, a figure that illustrates the speed of standard diffusion and the maturity of its technical community. This vitality is reinforced by the progressive convergence of several major industrial alliances.
Microsoft: The Copilot–MCP Convergence
Microsoft is today one of the pillars of MCP adoption. The protocol is now natively integrated into Windows 11, GitHub Copilot, and Copilot Studio, creating a continuum between development environments and conversational agents.
The Dataverse service, Microsoft's enterprise database, exposes an official MCP server, allowing agents to directly access business datasets without going through proprietary APIs.
Hugging Face: Open Standardization of AI Knowledge
Hugging Face has published its own official MCP server, providing access to the models, datasets, Spaces, and articles hosted on its platform.
This integration, compatible with Claude Desktop, VS Code, and Cursor, transforms the Hugging Face Hub into a universal contextual reservoir for MCP agents—a space where AIs can not only draw data but also interact with learning artifacts.
Google: Prudent but Strategic Adoption
Google has chosen a gradual approach.
While Gemini and Google Workspace already implement MCP-compatible protocols, the company continues to promote its own standard, A2A (Agent-to-Agent), in parallel. This cohabitation illustrates a strategy of balance: supporting interoperability while retaining a lever of influence on the inter-agent communication layer.
OpenAI: The Interoperability Turn
In 2025, OpenAI integrated MCP support into its Agents SDK, allowing GPT-4 and its successors to directly invoke tools via MCP servers. This move marks a profound strategic change: the market leader (with 35% enterprise adoption) now recognizes the value of a cross-vendor protocol—an implicit recognition that market growth will come through standardized coopetition, not proprietary lock-in.
Developer Ecosystem: The Living Base of the Standard
The most popular development tools—Cursor, Cline, Zed, Replit, Sourcegraph—have integrated the protocol. Around them, an ecosystem of community registries (mcp.so, MCP Market, PulseMCP) has been established, facilitating the discovery, rating, and sharing of servers.
These platforms function as the "App Stores" of the agentic world, where trust, traceability, and reputation become the new drivers of adoption.
Linux Foundation and Multi-Stakeholder Governance
To date, no official announcement confirms the hosting of the MCP under the aegis of the Linux Foundation or an equivalent structure.
However, many observers anticipate that as the protocol consolidates, a multi-company consortium will emerge to guarantee its neutrality, governance, and sustainability.
This future consortium could play a role analogous to that of:
- the Cloud Native Computing Foundation (CNCF) for Kubernetes,
- or the OpenSSF for open source software security.
Its missions would be threefold:
- Coordinate development efforts for reference implementations,
- Finance the maintenance and security of the protocol,
- Arbitrate technical evolutions and conflicts between contributors.
The current governance model—centered on Anthropic but open to external contributions—will have to evolve towards a collegiate structure.
This transformation will be crucial to credential the MCP as a global trust infrastructure, and not as a standard dominated by a private actor.
Security, Supply Chain, and Trust Registries
The open and distributed nature of the MCP, while promoting innovation, also creates a new field of vulnerabilities. In theory, anyone can publish an MCP server. In practice, this opens the door to supply chain risks: compromised, malicious, or simply misconfigured servers.
A recent study highlighted the extent of the problem:
- 43% of tested implementations exhibited command injection vulnerabilities,
- 30% were exposed to SSRF flaws,
- 22% allowed arbitrary file system access.
To respond to these threats, the developing MCP Registry will have to integrate:
- certification mechanisms based on digital signature,
- automated security audit,
- and server reputation systems (trust scoring).
Open source tools like mcp-scan are already emerging to detect vulnerabilities, dangerous configurations, or tool poisoning attempts (malicious injections hidden in tool metadata).
The open source ecosystem plays an essential role as a collective watch here. Initiatives like GitHub Security Advisories, OWASP AI Security Project, or AI Supply Chain SIG contribute to strengthening security practices around the protocol.
However, companies will also have to adopt rigorous internal procedures:
- whitelisting of authorized MCP servers,
- automated security scans in CI/CD pipelines,
- non-production sandbox testing before any deployment.
This combination of community tooling and corporate governance will shape the foundations of a secure, certifiable, and sustainable MCP ecosystem.
Predictions and Future Trajectories (2025-2030)
The Model Context Protocol (MCP) is entering a decisive structuring phase.
After the euphoria of early adoption, the 2025–2030 decade will see the emergence of an era of consolidation and industrialization, where the protocol will become a true global cognitive infrastructure.
Standardization and Maturity (2025-2026)
The next two years will mark the stabilization of the standard.
According to the community roadmap, version 2.0 of the MCP, expected for November 25, 2025, will integrate three structuring evolutions:
- native multimodality (text, audio, video, signals),
- bidirectional streaming for continuous data flows,
- and enhanced authentication compliant with OAuth 2.1 and DCR.
This stable version will constitute the necessary technical basis for large-scale production adoption.
In parallel, a set of compliance frameworks will emerge:
- standardized test suites to validate implementations,
- MCP server certifications guaranteeing security and interoperability,
- and a centralized registry aggregating server metadata, facilitating component discovery and verification.
Mirroring the role played by the CNCF Landscape in cloud native, the MCP Registry will become the central hub of the agentic ecosystem.
Proliferation of Agents and Multi-Agent Orchestration (2026-2028)
Starting in 2026, the generalization of Agentic AI will transform the very nature of information systems. Multi-agent architectures orchestrated via MCP will become the norm in complex environments.
New organizational models will emerge:
- a coordinator agent ("Chief of Staff") managing planning,
- surrounded by specialist agents (Coder, DevOps, Data Analyst, Legal Advisor, etc.),
- all interconnected via MCP and exchanging in real-time within a distributed mesh.
These Agentic Meshes will become the reference architectures for distributed cognitive systems, with fluid, traceable, and governable communication between each agent.
Major cloud players will quickly follow suit:
- AWS Lambda, Google Cloud Run, and Azure Functions will natively integrate the MCP protocol,
- enabling the serverless deployment of agents capable of mutual orchestration without going through proprietary APIs.
This convergence between cloud and agentic mesh will mark the fusion between infrastructure computing and adaptive intelligence.
Consolidation and Mature Ecosystem (2028-2030)
Between 2028 and 2030, the MCP will reach its industrial maturity.
The ecosystem will enter a phase of rationalization comparable to that experienced by the cloud in the early 2020s:
- The most efficient orchestration patterns will be standardized.
- Reusable libraries of servers and agents will emerge.
- Multi-tool servers, capable of exposing several capabilities in a single module, will replace single-function implementations.
- Companies will turn to packaged and certified solutions, prioritizing security and compliance over artisanal flexibility.
The MCP will also evolve through integration with other structuring technologies:
- Blockchain and smart contracts for traceability and trust between agents.
- Edge computing to bring agents closer to the data they process (industry, health, IoT).
- Post-quantum cryptography to secure long-term communications.
These synergies will transform the MCP into the backbone of a distributed intelligence ecosystem, interconnecting billions of agents and heterogeneous systems.
Economic Value and Industrial Impact
The economic impact of the MCP will be considerable. According to McKinsey, interoperability between AI systems—of which the MCP is the keystone—could generate up to 300 billion dollars in annual added value in critical infrastructures by 2030.
This value will primarily come from:
- reduced integration costs,
- automation of high-cognitive-intensity tasks,
- and mutualization of models across partner ecosystems.
But maturity will not come without risks.
According to Gartner, by 2027, more than 40% of agentic AI projects could be abandoned due to a lack of clear return on investment or adequate governance.
The MCP, by reducing integration complexity and structuring interaction flows, has the potential to reverse this trend—provided that companies adopt a disciplined approach:
- define tangible KPIs (time saved, resolution rate, value generated),
- measure traceability and operational performance of agents,
- and invest in the training and human supervision of systems.
Conclusion: Towards an Open and Governed Agentic Web
The Model Context Protocol (MCP) is more than just a technical standard: it is a civilizational infrastructure for distributed intelligence—the equivalent, for the agentic era, of what HTTP was for the content web.
It constitutes the interoperability layer without which artificial intelligence systems would remain confined to isolated environments, unable to cooperate or self-organize.
Its evolution towards a mature ecosystem—integrating multimodality, trust registries, distributed governance, and cross-vendor interoperability—will determine the very form of the global agentic web.
Its structuring will determine the possibility of an intelligent agent network capable of collaborating, learning, and reasoning on a planetary scale without compromising security or human responsibility.
The stakes of the MCP far exceed the field of software engineering. The goal is now to build an open, secure, and ethical agentic web, where artificial intelligences can interact while respecting:
- data sovereignty,
- protection of private life,
- and the democratic values on which our societies are based.
In this new paradigm, transparency, traceability, and shared governance are no longer options but foundations. The question is no longer just how agents will communicate, but under whose authority, according to which rules, and serving what ends.
The MCP is not an end, but the foundation of a profound recomposition of digital systems. It opens the way to a new era—one where AI agents cooperate in governed networks, capable of collective reasoning, distributed action, and iterative learning.
Organizations that master this infrastructure—while anticipating its ethical, regulatory, and geopolitical challenges—will occupy a determining position in the decade ahead. They will not merely use AI: they will become its systemic architects.