Model Context Protocol (MCP) by Anthropic is an open specification proposed by Anthropic, which enables AI models, agents, and supporting infrastructure to share and manage context. MCP defines a set of message formats and APIs that formalize how context, which can include state, instructions, or data, is communicated between language models, intermediary servers, gateways, and connected services.
The aim is to make the entire process of prompt composition, tool use, and multi-agent orchestration reliably interoperable, regardless of back-end or deployment environment. This protocol goes beyond simple API requests by supporting complex, evolving scenarios such as multi-step workflows, tool calling, and persistent memory across user sessions.
MCP’s formalized approach helps developers build systems where context can move fluidly between components, reducing friction and enabling richer, more capable AI applications. It is a foundational standard for scalable agentic systems where consistent context flow is critical for maintaining state, intent, and utility over time.
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Understanding the value of MCP requires looking at how context handling shapes the behavior and usefulness of AI systems. As models grow more capable and workflows become more complex, consistent and interoperable context management becomes essential:
Enables interoperability across systems: MCP provides a standardized way for different tools, models, and services to exchange context. This removes the need for custom integrations between components and allows developers to mix and match infrastructure without breaking compatibility.
Supports multi-agent collaboration: MCP makes it easier to coordinate multiple agents working on a task by maintaining a shared, evolving context. This enables parallelism, delegation, and specialization in agent workflows.
Improves tool use and orchestration: By formalizing how tools and models share information, MCP helps orchestrate complex tool use, including dynamic tool selection, parameter passing, and result tracking, key features for building advanced agentic systems.
Enables long-term memory and state: MCP supports persistent context across sessions, allowing AI systems to maintain memory of previous interactions. This is vital for continuity in personal assistants, enterprise agents, and long-running workflows.
Simplifies development and debugging: With a clear specification, developers can more easily reason about, test, and debug context flow. This reduces hidden bugs and unexpected behaviors due to inconsistent or missing context.
Lays groundwork for scalable architectures: As AI deployments scale, consistent protocols like MCP help ensure systems remain maintainable, extensible, and reliable over time, even as complexity grows.
MCP was introduced by Anthropic as an open standard for interoperable context sharing.Learn more in our detailed guide to MCP Anthropic.
Core Principles of the Model Context Protocol
Context Sharing Across AI Systems
MCP is built to standardize context sharing across AI agents and systems, ensuring that actionable knowledge, memory, and state can be consistently transferred as required. Unlike isolated, stateless LLM calls, MCP enables multi-agent workflows where each participant can receive, interpret, and update the shared context. This cross-system compatibility unlocks collaborative scenarios, such as delegated tasks or collaborative planning between autonomous agents, without losing track of user intent, history, or relevant data.
MCP’s approach allows context to persist and evolve across sessions and transactions. By providing a repeatable, machine-readable format for context, developers can design flows where state is maintained over time, tasks can be resumed mid-way, and downstream processing does not lose fidelity.
Extensibility and Modularity
The protocol is engineered with extensibility in mind. MCP supports modular adoption, where new context types, tools, or system capabilities can be introduced incrementally. By decoupling core schema from business-specific extensions, implementers can tailor context payloads to their unique domain requirements while withstanding future upgrades or integration with third-party systems.
This design principle reduces vendor lock-in and future-proofs investments in AI infrastructure. MCP’s modularity extends to its runtime behavior. Components can selectively process or ignore context elements not relevant to them without breaking protocol compliance. This makes it easier to upgrade single parts of a complex AI system without wholesale rewrites, and also simplifies the integration of agents or toolchains from different vendors.
Security and Trust Boundaries
Security and trust are core design criteria for MCP, given the sensitive nature of information AI agents often handle. The protocol supports explicit trust boundaries, allowing administrators to govern which systems or agents can access or mutate context fields. This minimizes the risk of data leakage or unauthorized escalation, especially in systems spanning multiple organizations or trust domains.
MCP’s formalized security features include mechanisms for attestation, signature verification, and audit logging of context exchanges. This enables compliance with privacy requirements and operational policies, making MCP suitable for regulated industries and enterprise deployments. Having a standard way to enforce and verify trust boundaries increases auditability.
One of MCP’s key strengths lies in its handling of data flow and state management. The protocol enables robust transmission of both ephemeral and persistent context, allowing AI components to model current state, user goals, environmental inputs, or working memory over time. Instead of ad-hoc session tracking, MCP provides a structured, versioned container for passing and updating state as workflows progress.
This approach unlocks consistent multistep execution, error recovery, and context recovery after interruptions. State management inside MCP is granular and can be partitioned, enabling independent modules to read, write, or lock portions of context according to their operational needs.
The Architecture of MCP
The architecture of the Model Context Protocol (MCP) is designed around a client-server model that enables structured, extensible, and secure exchange of context between AI systems. At a high level, MCP defines both the roles that components play in context exchange and the layered protocol that governs their interaction.
MCP host: This is the primary AI application (e.g., Claude Desktop or Visual Studio Code) that coordinates context use and manages one or more client connections.
MCP client: Each client connects to a specific server and acts as the bridge between the host and that server. Clients are instantiated on a per-server basis, maintaining one-to-one relationships with servers.
MCP server: A server provides the actual context, such as tools, data, or prompts, to the client. Servers may be local (communicating via standard I/O) or remote (communicating over HTTP).
This architecture enables a host application to interact with multiple servers concurrently. For example, a host might simultaneously connect to a filesystem server, a database server, and an error-reporting server, each through its own client.
Protocol Layers
MCP is structured into two distinct layers: the data layer and the transport layer.
Data layer
This is the core of MCP and defines the JSON-RPC 2.0-based communication protocol. It includes:
Lifecycle management: Handles initialization and shutdown of connections, as well as capability negotiation.
Server primitives: Define what context a server can provide: tools (invokable functions), resources (data), and prompts (structured templates).
Client primitives: Enable the server to interact with the host AI, including sampling (language model calls), elicitation (user input requests), and logging.
Notifications: Allow for real-time updates between servers and clients without requiring responses.
Transport layer
This layer manages how messages are physically transmitted between clients and servers. MCP supports two transport types:
stdio transport: Optimized for local communication between processes on the same machine.
Streamable HTTP transport: Supports remote communication and integrates with standard authentication mechanisms like OAuth and API tokens.
Both transport methods abstract away transmission details while maintaining a consistent data protocol across implementations.
Data Layer Protocol and Primitives
The data layer’s core value lies in its primitives, which define the types of context that can be shared:
Tools: Callable functions like file operations or API requests.
Resources: Contextual data such as document contents or database schemas.
Prompts: Templates for guiding LLM behavior, such as system instructions or few-shot examples.
These primitives support methods for listing, retrieving, and executing (where applicable), allowing for dynamic discovery and use. For example, a server might list available tools (tools/list) and allow the host to invoke them (tools/call).
Client primitives extend interactivity by letting servers delegate tasks like generating completions (sampling/complete) or requesting user input (elicitation/request) to the host AI application.
Local and Remote Deployment
MCP servers can be deployed either locally or remotely, depending on their transport layer. Local servers use standard input/output for fast, low-latency communication. Remote servers communicate over HTTP and can integrate with broader infrastructure platforms. This flexibility allows MCP to support diverse environments while maintaining a unified protocol.
By organizing context exchange into a layered, modular architecture with well-defined roles and extensible primitives, MCP enables scalable and reliable integration of AI capabilities across tools, services, and workflows.
Model Context Protocol vs. Similar Concepts
Model Context Protocol vs. RAG
Retrieval-Augmented Generation (RAG) improves LLM outputs by injecting context retrieved from external databases, typically right before generating a model response. RAG focuses on enriching prompts with relevant data, usually per-call, while MCP governs the ongoing, bidirectional management of context across agents, tools, and sessions. MCP handles complex state persistence, mutation, and versioning, whereas RAG is mainly about fetching and supplying information just in time for inference.
MCP’s design excels where workflows need to track session state, process multi-step tasks, or coordinate several AI components asynchronously. RAG may provide short-lived, stateless context, but it rarely manages session memory or handles tool-driven workflows natively. For applications demanding workflow continuity, memory, or agent orchestration, MCP is the more comprehensive framework.
Model Context Protocol vs. A2A
Agent-to-agent (A2A) protocols provide direct channels for autonomous AI agents to communicate, typically to delegate tasks or negotiate outcomes. While A2A establishes the basics for agent interaction, it often lacks the standardized, persistent context management infrastructure found in MCP. MCP delivers a broader, infrastructure-level solution for context flow, tracking both inter-agent communication and the shared session, state, and authorization logic crucial for complex operations.
Unlike minimalist A2A schemes, MCP accommodates workflows where agents, toolchains, and users may interact over days or weeks, with evolving requirements and data. Its structured messages, context layering, and policy hooks serve both communication and long-term state management, making it more appropriate for business-critical or regulated workflows than basic A2A models.
Function calling provides a way for LLMs to trigger external code or tools based on recognized API schemas within a prompt. While function calling supports limited context, usually based on the immediate user request, MCP governs the broader, persistent, and multi-participant context lifecycle. MCP models not just invocation but also prior state, expected results, error handling, and ongoing collaboration between multiple agents and tools.
Function calling often depends on one-off, stateless exchanges, and may not standardize how intermediate context, progress, or partial results are tracked. MCP sets a richer protocol for managing end-to-end context, enabling workflows that span multiple actions, corrections, or actors. For advanced orchestration where context from multiple invocations or agents must be coherently maintained and transferred, MCP offers the more scalable solution.
Key MCP Use Cases
Multi-Turn Conversational Agents
MCP supports conversational agents that require persistent, multi-turn context. Traditional chatbots struggle to maintain continuity over extended sessions or complex, branching discussions. With MCP, session history, user goals, and environment data are serialized and updated on each exchange, allowing agents to remember and act on past interactions, shifting intent, or long-term objectives.
This persistent memory and layered state management also enable advanced dialog management features. Agents can track unresolved questions, parallel threads, or shift context between different tasks within the same conversation. As a result, user experience improves through better recall, follow-up, and continuity.
Many production AI scenarios require chaining together several tools or agents as part of a coordinated workflow. MCP supports stepwise orchestration, where each tool or sub-agent receives relevant context and updates the shared state with outputs, decisions, or failures. This enables complex use cases like multi-step information extraction, automated research assistants, or document processing pipelines, relying on a single, authoritative context record.
The structure provided by MCP promotes resilience and recoverability: if a workflow is interrupted or an error occurs, state can be inspected, rolled back, or resumed thanks to MCP’s granular state serialization. As a result, agentic workflows backed by MCP are less prone to brittle, ad-hoc implementations and can more easily support auditability.
Business System Integration
Enterprises integrating AI with legacy or heterogeneous IT systems benefit from MCP’s standardization of context. Through MCP-compliant gateways and servers, AI agents can interact with databases, CRM platforms, and custom business logic using context payloads that respect enterprise security and data governance requirements. Context partitioning ensures sensitive information is kept within prescribed boundaries, while non-sensitive data flows freely between modules or external partners as required.
This structured approach simplifies regulatory compliance and enables consistent auditing of interactions between AI and core business systems. MCP also eases the integration of third-party SaaS or cloud services, as all participants can interpret, update, and secure context according to the protocol.
Development assistants and code execution platforms gain robustness from MCP’s state management capabilities. When running code, managing multi-step refactoring, or assisting with complex debugging, the continuity of context matters as much as output correctness. MCP can serialize the current project state, user preferences, error histories, or in-progress code reviews, so LLMs and allied tools always work with the latest, synchronized context.
This opens the door to richer collaboration between human developers and AI agents or between multiple autonomous tools in the same build pipeline. By ensuring accurate state transfer, error handling, and session recovery, MCP-based development assistants provide more predictable, reliable, and secure automation.
Notable Model Context Protocol Servers and Gateways [QG3]
As the Model Context Protocol (MCP) ecosystem expands, numerous gateways and servers have emerged to demonstrate the protocol’s versatility across tools, workflows, and AI systems. These implementations act as bridges between language models and various data or execution environments, showcasing how MCP can unify context exchange across heterogeneous systems.
Below are some notable MCP gateways and servers developed or maintained by Anthropic and the broader open-source community.
Notable MCP gateways:
Obot: One of the first MCP gateways designed to enable seamless integration between Anthropic’s Claude models and diverse third-party services. Acting as a bridge, Obot enables users to connect their Claude desktop or API environments to external systems, such as databases, development tools, or SaaS applications, using the MCP framework. Learn more about Obot!
ContextForge gateway: Provides a registry, proxy, and unified endpoint in front of MCP servers and REST/A2A services, handling auth, federation, transport, and observability.
Lasso Security MCP Gateway: Open-source enterprise-grade gateway emphasizing guardrails, policy enforcement, and security controls for MCP tool invocations (community solution).
Envoy AI Gateway (with MCP support): Enterprise gateway extending the Envoy platform to include Model Context Protocol routing, observability, and policy enforcement for large-scale AI workloads.
⬇️ Download the Obot open-source gateway on GitHub and begin integrating your systems with a secure, extensible MCP foundation.
Notable MCP servers:
There are thousands of MCP servers available, many of them are listed here. Below are several MCP servers offered by notable software companies:
GitHub MCP server: Lets AI agents access repositories, issues, and CI/CD pipelines using natural language. Supports code analysis, automation, and developer collaboration.
Cloudflare MCP server: Connects AI tools to Cloudflare services like Workers and Observability. Enables real-time debugging, config updates, and infrastructure queries.
Datadog MCP server: Provides AI access to logs, metrics, traces, and incidents via natural language. Simplifies monitoring and incident response in operational workflows.
Sentry MCP server: Integrates Sentry issue and performance data into MCP environments. Agents can fetch errors, resolve issues, and access project context securely.
Figma MCP server: Exposes design components, styles, and metadata to LLMs. Helps generate code aligned with design files, improving dev-design collaboration.
Every context payload should be validated against agreed schemas before processing or routing to downstream services. This ensures that context structure, data types, and required fields are always present and correctly formatted, reducing runtime failures rooted in malformed or unexpected input. Using automated validation tools or schema-first development approaches also accelerates onboarding and error diagnosis.
Beyond correctness, schema validation acts as a security and trust mechanism, blocking injection of unapproved context fields or data. It becomes easier to reason about protocol conformance, enforce contracts between modules, and future-proof the system against schema drift as teams or partners extend the protocol.
2. Separate Context Layers from Business Logic
A clean separation between context handling and business logic prevents coupling that can lead to brittle or hard-to-debug systems. MCP’s context formats and exchanges should be managed by dedicated middleware or orchestration layers, not by embedding protocol logic directly within domain services. This modularity preserves maintainability and allows business rules to evolve independent of changes to context standards or schema updates.
Isolating context logic also enables easy extension, swapping components, or parallel testing of new protocol versions. Teams can adopt new MCP schema elements, upgrade context servers, or integrate with external agents without having to untangle or refactor core application logic. This layered architecture is particularly important for larger teams or projects with iterative, multi-phase development cycles.
3. Test Server–Client Compatibility Early
Server–client compatibility is vital when deploying MCP in real applications. Differences in protocol implementation, message serialization, or schema interpretation can lead to subtle bugs, broken workflows, and inconsistent state. It is best practice to establish continuous integration tests that ensure each change to server, gateway, or agent logic is checked for compatibility against client code and real MCP traffic.
Simulating diverse real-world scenarios, such as partial context transfer, concurrent updates, or slow connection recovery, can help surface edge cases early. Ideally, automated regression suites test not just happy-path functionality but also persistence, security, and error recovery features within MCP-driven systems.
4. Leverage Versioning Strategically
Versioning is a key tactic for managing change within MCP-driven ecosystems. Context schemas, API endpoints, and protocol semantics should be versioned to allow gradual migration and ongoing interoperability between old and new components. This helps teams roll out feature improvements or security updates incrementally, minimizing disruption while maintaining backward compatibility.
Strategic versioning is not just about communication of protocol changes. It also underpins governance, testing, and troubleshooting by providing a clear audit trail of how context formats and contracts have evolved over time. Proper versioning frameworks, alongside explicit deprecation policies, reduce the risk of fragmentation or incompatible upgrades as MCP infrastructure matures.
5. Monitor and Log Context Exchanges
Monitoring and logging are essential for both operational insight and post-incident analysis within MCP implementations. Every context exchange, whether between clients, agents, or servers, should be logged with sufficient fidelity to trace flows, debug issues, and audit for compliance. This includes tracking who initiated the exchange, what data was transferred, and any policy or schema violations encountered.
Effective monitoring also supports performance tuning and usage analysis by providing metrics on context volume, processing latency, and common error patterns. These logs form the backbone of both operational dashboards and compliance audits, helping organizations detect anomalies, respond to incidents, and optimize resource allocation. Related content: Read our guide to MCP tools
