What is Model Context Protocol (MCP)
Model Context Protocol, or MCP, is a modern, lightweight communication protocol built on top of JSON-RPC 2.0. It’s specifically designed to help AI models, tools, and services talk to each other in a way that’s clear, structured, and context-aware.
Unlike traditional APIs that focus purely on inputs and outputs, MCP introduces a shared language for describing what a tool can do, how it should be called, and how it returns results—all while preserving context. It’s not just about sending and receiving data; it’s about enabling tools to express their capabilities, understand intent, and return responses that can be used meaningfully in multi-step processes.
MCP helps solve a growing challenge in agentic and modular AI systems: how do you dynamically connect a model with tools it hasn’t seen before, pass along relevant context, and execute actions reliably?
By introducing well-defined methods like describe model, invoke, and status, MCP allows systems to be both self-discoverable and self-executing. This makes it an ideal foundation for building intelligent toolchains, autonomous agents, or service orchestration layers—where components need to discover, call, and coordinate with one another on the fly.
To successfully integrate Model Context Protocol (MCP), developers must define schemas, support context passing, and structure agent calls.
It’s flexible enough to be embedded into fast prototypes, but structured enough to support production-grade systems.
Why MCP Matters in Agentic AI, Automation, and Protocol Standardization
As systems become more intelligent and decentralized, the way they interact must evolve beyond traditional API calls. In modern architectures—where AI agents invoke tools, tools return rich outputs, and models work in coordinated sequences—what’s needed is not just a communication channel, but a shared understanding of capabilities, context, and intent.
That’s where MCP comes in.
In agentic AI, tools are no longer passive endpoints—they are participants. An agent might need to discover what a tool can do, decide how to invoke it based on current context, and pass that context forward to another model or agent. MCP enables this by defining a contract between components—clearly stating what actions are available, what structure the inputs should follow, and what kind of output will be returned.
This isn’t just about convenience; it’s about creating trustworthy, reproducible, and extensible interactions between intelligent components. Without a protocol like MCP, engineers are forced to hardcode logic, document it manually, and rely on fragile assumptions across interfaces.
In automation, MCP becomes a way to abstract tooling—so workflows become flexible and systems can adapt at runtime. Instead of relying on tightly-coupled integrations, developers can plug in new services and agents that follow the same protocol, immediately making them discoverable and usable by the larger system.
And from a protocol standardization standpoint, MCP introduces a structured, consistent interface that can be adopted across tools, regardless of language or platform. Whether it’s a cloud-native service, a Python tool, or a remote AI endpoint, MCP offers a uniform approach that promotes interoperability, testing, and long-term maintainability.
In short, MCP matters because it enables scalable, intelligent collaboration between components—without reinventing communication every time.
Use Cases: LLM-Powered Agents, Network Automation, API Gateways, RAG Systems,
MCP isn’t just a theoretical upgrade—it solves real-world problems across multiple domains where intelligent systems need to talk to each other with clarity and purpose.
In LLM-powered agent systems, models often need to invoke external tools—calculators, search functions, databases, APIs, or other models. MCP allows these tools to expose their capabilities in a discoverable way. The agent can query what the tool can do, select an action, provide context, and invoke it—all dynamically, without needing to hardcode tool logic.
In network automation, MCP enables intent-driven orchestration. Think of a situation where a telemetry model observes bandwidth usage, passes that context to a policy evaluation model, which in turn invokes a remediation tool. MCP provides the glue that links these actions together with traceable, self-descriptive messages—without brittle CLI parsing or static integration.
In API gateway systems, MCP introduces a new layer where APIs don’t just respond to fixed endpoints—they declare what they can do via structured descriptions. Gateways can route requests based on capabilities rather than URLs, enabling more flexible and intelligent mediation across services.
For RAG (Retrieval-Augmented Generation) pipelines, MCP makes it possible to modularize each stage—retrievers, rankers, chunkers, and generators—into callable services with clear interfaces. You can chain actions where one model’s output becomes the next model’s context, and every tool can declare exactly how it expects to be called and what it will return.
These examples show that MCP is not bound to a single domain or stack—it’s designed to be composable, extendable, and adaptable.
What the Reader Will Gain – How to Integrate Model Context Protocol (MCP) in AI Agent Systems
This guide is not just about understanding what MCP is—it’s about giving you the ability to integrate it end-to-end in AI Agent Systems
By the end of this blog, you’ll have a working knowledge of: Step-by-Step Guide to Integrate Model Context Protocol (MCP)
- How MCP is structured, and what role each method plays (describe model, invoke, status)
- How to design, implement, and test MCP-compatible services
- How to pass and preserve context across tool chains
- How to secure and extend MCP for production-grade systems
Whether you’re building AI workflows, automating services, or designing modular tooling, this guide aims to give you the tools, patterns, and confidence to bring MCP into your own architecture—cleanly, intelligently, and with minimal overhead.
MCP Architecture Overview
Before we dive into the how-to, let’s take a moment to understand what’s really going on under the hood when you integrate MCP.
Think of MCP as a smart translator sitting between your AI models and external tools. It uses JSON-RPC 2.0 as the transport layer—but adds its own structure and logic so that agents can not only call tools, but also understand what those tools are, what they expect, and what they return.
At the heart of MCP, there are three primary roles:
- Client – the initiator of the request (often an AI model or controller).
- Server – the tool or service that performs the action.
- Protocol – the agreed-upon format for describing, invoking, and communicating status.
The beauty of MCP is that it makes every tool self-descriptive. A client can ask, “What can you do?” and the tool can respond with a full list of its capabilities, expected inputs, and output types. From there, the client can invoke actions using context-rich instructions and receive structured results—all with minimal ambiguity.
Now let me walk you through the core components of MCP’s architecture and how they fit together like clean, well-labeled Lego blocks—ready for scalable and intelligent systems design
Before you integrate Model Context Protocol (MCP), make sure your environment supports JSON-RPC and a lightweight web framework like FastAPI or Flask.
This architecture becomes even more powerful when you integrate Model Context Protocol (MCP) into a multi-agent AI environment.
Prerequisites and Environment Setup
Before we begin implementing MCP, it’s important to ensure your development environment is ready to support a clean and functional integration. MCP is lightweight by design, but like any protocol-driven system, it benefits from a clear baseline setup that supports structured communication, testing, and observability.
This section outlines the required tools, libraries, and environment components you’ll need to get started.
Technical Environment Required
1. Python 3.9+
Python serves as a flexible and readable base language for this setup. Most examples and utility packages used in this guide will assume Python 3.9 or later for compatibility with recent library versions.
Make sure to create a virtual environment to isolate your MCP environment from other projects.
2. pip (Python Package Installer)
We’ll use pip to install the key JSON-RPC packages and optional web frameworks.
Core Libraries and Frameworks
3. FastAPI or Flask (Web Framework)
MCP communication is typically exposed over HTTP using a lightweight web server.
You can choose between:
- FastAPI – Modern, async-ready, excellent for rapid development
- Flask – Lightweight, stable, synchronous, and minimal
Install either (or both, for comparison):
4. jsonrpcserver / jsonrpcclient
These libraries implement the JSON-RPC 2.0 specification and make it easy to define handlers and invoke methods programmatically.
Optional but Recommended
5. Docker
For containerized environments or deployments, Docker allows you to run MCP tools in isolated environments. It’s especially useful if your agents or tools are going to be deployed independently in production.
Install Docker: https://docs.docker.com/get-docker/
Development & Testing Tools
6. Postman
Postman is a great tool for sending and debugging JSON-RPC HTTP requests manually.
You can use it to test your describe_model and invoke endpoints.
- Set the HTTP headers to Content-Type: application/json
- Format JSON-RPC payloads correctly to simulate real client
7. Visual Studio Code (VS Code)
Any IDE will work, but VS Code is particularly useful for its extensions, built-in terminal, and JSON formatting.
8. ngrok (for exposing localhost)
If you need to make your local MCP server available to external services (e.g., testing an agent’s ability to call your tool), ngrok can expose your localhost securely.
Sample Repo or GitHub Template (Optional)
While this guide is designed to be implementation-first, you may optionally use a base repository structure to jump-start your integration.
You can clone or structure your directory like this:
You will be building this structure incrementally as we walk through the implementation.
4.1 Define the Schema for to Integrate Model Context Protocol (MCP)
The first and most important step in implementing MCP is to define the schema that describes your model or tool. This schema acts as the contract between the tool and any agent or client attempting to interact with it. Without this, your tool remains opaque—usable only by those who already know how it works.
MCP uses the describe model method to expose this schema, allowing clients to understand:
- What the tool is
- What actions (functions) it supports
- What inputs those actions require
- What outputs they return
- What context or constraints apply
What Should the Schema Include?
At a minimum, your schema should describe:
- Tool Name and Description
- Version
- Methods
Each method should define:
- name
- description
- parameters (with types, required/optional, descriptions)
- returns (with structure and expected data types)
Sample MCP Schema (JSON Format)
Here’s a simple example of an MCP-compliant schema for a fictional text utility tool:
Define: Model, Intent, Context, Input, and Output
Here’s how each piece plays a role in the schema:
- Model: A unique identifier for your tool or service (e.g., text_utils, network_configurator).
- Intent: The purpose or objective the caller is trying to achieve. In MCP, intent is implied by the method name and description. For example, the intent might be to “summarize text” or “fetch device status.”
- Context: A structured object representing the state or prior results that may influence the tool’s behavior. Context is optional, but critical when chaining tool calls in agent workflows.
- Input: Parameters required by a method. Clearly defined types, constraints, and descriptions allow for accurate invocation.
- Output: The return structure. Should be predictable and structured for downstream usage.
Each method in the schema should encapsulate these five dimensions—making it self-contained, explainable, and ready for dynamic discovery.
Schema Formats: OpenAPI, YAML, or JSON
Regardless of format, the structure should remain consistent when you integrate Model Context Protocol (MCP) into your tool’s describe_model method.
The MCP specification is format-agnostic, but in practice, it supports and encourages using structured schema formatslike:
- JSON Schema – Useful for direct parsing and validation.
- OpenAPI 3.0+ – More expressive; widely adopted for API documentation.
- YAML – Readable alternative to JSON for config-driven environments.
Here’s the earlier example rewritten using OpenAPI-style YAML for clarity:
4.2 Build Your JSON-RPC Server (LLM Agent Side)
Once the schema is defined, the next step is to expose your tool’s capabilities to the outside world using a JSON-RPC server. This is the interface that models, agents, or external systems will communicate with to discover, invoke, and query the status of your tool.
We’ll use FastAPI in this example, but you can adapt the same logic to Flask or any other Python web framework.
Setup FastAPI + JSON-RPC
Install the required packages (if not already done)
Now, let’s start by setting up a basic FastAPI app that listens for JSON-RPC requests:
Implement MCP Methods
1. Describe_model
This method responds with the schema we created earlier.
2. Invoke
This method executes a requested function.
3. Status
Optional, but useful for checking execution or agent state.
Return Structured Responses with Metadata
MCP encourages returning structured JSON objects with consistent fields like:
You can embed metadata in every result if desired for traceability.
Error Handling (MCP-Compliant Faults)
Use JSON-RPC-compliant error formats when input is invalid or execution fails:
Once you’ve built the core JSON-RPC endpoints, you’re ready to integrate Model Context Protocol (MCP) into any AI or microservice-based infrastructure.
Run the Server
Run it locally with: “ uvicorn app:app –reload –port 8080 “
Once live, your tool will accept POST requests at /, accepting JSON-RPC payloads for describe model, invoke, and status.
Quick Test with curl:
You can explore the complete specification of JSON-RPC here to understand how MCP builds on it.
To integrate Model Context Protocol (MCP) from the client side, you can either use the jsonrpcclient library or send raw HTTP requests.
4.3 Build JSON-RPC Client (Tool or Interface Layer)
With the MCP server up and running, the next step is to build a client that can send requests and process responses according to the MCP specification. This client could be part of an AI agent, a backend microservice, a web-based orchestrator, or even a CLI tool.
The goal is simple: send well-formed describe model, invoke, or status requests—and handle responses or errors gracefully.
Use json rpc client or Standard HTTP Client
You have two good options here:
- Option 1: Use the json rpc client library (recommended for ease and standards compliance)
- Option 2: Use requests for full control and transparency
Install both (for flexibility):
E.g “ pip install jsonrpcclient requests”
Option 1: Using jsonrpcclient
Here’s how you can use the high-level client library to call your MCP tool:
Option 2: Using requests with raw JSON-RPC
This gives you more visibility into what’s being sent/received:
Processing MCP-Compliant Responses
Every MCP response follows the JSON-RPC format:
To extract the data:
Handle errors using:
This completes the client-side setup. Your client can now:
- Discover tool capabilities (describe_model)
- Invoke actions (invoke)
- Check tool readiness (status)
You now have both ends of the MCP protocol implemented—ready to be chained into agent workflows, AI platforms, or automation pipelines.
4.4 Enable Context Passing and Intent Execution
One of the defining strengths of MCP is its ability to preserve and forward context between method calls. This enables systems where a single agent or model can call multiple tools in sequence, make decisions based on prior outputs, and dynamically adapt based on real-time results.
In this section, we’ll cover how to implement and utilize the context parameter in MCP to support intent execution, state awareness, and multi-step workflows.
What is Context in MCP?
When you integrate Model Context Protocol (MCP), context becomes the thread that links one invocation to the next.
- Outputs from previous method calls
- Identifiers (session ID, user ID, task ID)
- Execution metadata (timestamps, flags, scoring)
- Dynamic conditions or environmental variables
This allows a method invocation to “know what just happened,” rather than operating stateless.
Example Scenario
Let’s say you’re building a multi-step agent that:
- Extracts device details from a config
- Queries device status
- Suggests an action based on policy
Each of these steps is handled by a separate tool, but all require shared context.
Context Structure Example
Here’s a typical MCP context object:
How to Handle Context in invoke
Update your invoke handler to receive and use the context parameter:
Executing Chained Intents
Here’s how a client might pass context between tools:
4.5 Logging, Observability & Debugging
Once your MCP server and client are working, the next step is to make sure they’re observable and debuggable. In real-world deployments—especially with multiple chained tool calls, evolving contexts, and agent-based logic—understanding what happened and when is essential.
This section focuses on strategies to implement clear logging, structured observability, and robust debugging for your MCP setup.
Structured Logging
Instead of writing plain text logs, structure your logs in JSON or key-value format. This makes it easier to query, index, and trace across systems.
Example (Python log entry):
5. Security Considerations
When you integrate Model Context Protocol (MCP) into external-facing tools, HTTPS becomes mandatory to secure payloads and token exchanges.
Model Context Protocol (MCP) is transport-agnostic by design, but when deployed in real-world environments—especially those involving sensitive tools, data pipelines, or multi-tenant access—security becomes non-negotiable.
Whether you’re exposing your MCP server within a trusted mesh or to external systems, the protocol must be protected at multiple layers. This section outlines how to secure MCP implementations in a modern production setting.
JSON-RPC over HTTPS
The first line of defense is to ensure transport-level encryption.
Even though MCP runs over HTTP via JSON-RPC, you should never expose an unsecured endpoint. All communications must be tunneled through HTTPS to protect:
- Payloads containing sensitive inputs or outputs
- Context data that may include user information or system metadata
- Authentication tokens used in headers
Steps to enforce HTTPS:
- Use reverse proxies like Nginx or Traefik with TLS certificates (e.g., Let’s Encrypt)
- If deployed in cloud, enforce HTTPS through load balancer configurations
- Disable direct access to unencrypted backend ports
Token-Based Authentication (JWT / OAuth)
MCP endpoints should not be anonymously callable—especially invoke.
Implement token-based access control so that only trusted clients can:
- Discover model capabilities
- Execute tool methods
- Access contextual workflows
Recommended strategy:
- Use JWT (JSON Web Token) or OAuth2 bearer tokens
- Secure tokens in HTTP headers (Authorization: Bearer <token>)
- Validate each token on every request
Rate-Limiting & API Gateway Enforcement
To prevent abuse, overuse, or denial-of-service (DoS) attacks:
1. Apply rate limits at the API gateway or reverse proxy:
- Example: 100 requests per minute per client
- Use tools like Nginx, Envoy, or API management platforms
2. Set concurrency controls:
- Limit simultaneous invocations per client or session
- Protect downstream resources like GPUs or databases
3. Use an API Gateway to enforce:
- Authentication and authorization rules
- Endpoint-specific rate policies (e.g., tighter rules for invoke)
- Audit logging and metrics collection
6. Extending MCP – Multi-Agent Systems
MCP works exceptionally well for single tools—but its real power shines when used in multi-agent environments, where different services or models collaborate to complete tasks dynamically. In these scenarios, MCP serves as the unifying protocol that enables agents to discover, delegate, and coordinate actions through structured JSON-RPC messaging.
This section explores how to extend MCP to enable intelligent orchestration across multiple tools or services, forming a semantic mesh of autonomous agents.
Building a Mesh of JSON-RPC-Enabled Agents
In a multi-agent system, each agent is often implemented as a separate service with its own responsibilities. These agents could be:
To integrate Model Context Protocol (MCP) across these agents, each must expose a common schema and support structured RPC calls.
- Domain-specific tools (e.g., translator, analyzer, optimizer)
- Function-specific models (e.g., NER model, summarizer, evaluator)
- System-level controllers (e.g., planner, router, retriever)
Each of these agents runs its own MCP server, exposing:
- describe_model – for dynamic discovery
- invoke – to execute context-driven actions
- status – to indicate availability or current workload
Using MCP, these agents form a mesh—a flexible, scalable topology where agents can be invoked based on their capabilities and contextual fit.
Role of the Coordination Layer
While each MCP agent is autonomous, coordination becomes essential when:
- Tasks need to be split across multiple agents
- Subtasks depend on the output of previous ones
- Execution paths must adapt dynamically based on conditions
This is where a Coordinator Agent or Orchestration Layer comes in.
Responsibilities of the coordinator:
- Query describe_model across available agents to map capabilities
- Decide which agent should handle which intent
- Pass and enrich context as actions are invoked in sequence
- Handle fallback, retry, or multi-call chaining logic
In practice, this coordinator may also maintain a registry or agent catalog to route requests based on metadata like agent type, region, load, or version.
Supporting Subcalls and Delegation Logic
Subcalls refer to the ability of one agent (or the coordinator) to call another agent as part of executing a higher-level intent.
For example:
- Planner Agent receives a task: “Create a device report”
- It delegates:
- Call analyzer_agent.invoke(“extract_metrics”)
- Call formatter_agent.invoke(“generate_report”)
- The coordinator passes context between them automatically.
MCP makes this smooth because each invocation is:
- Self-describing
- Context-passing
- Protocol-compliant
Subcalls may be:
- Synchronous – the main agent waits for a result
- Asynchronous – coordinator handles response routing later
To implement this:
- Maintain consistent session_id or trace_id across subcalls
- Log the invocation chain for observability
- Optionally add a delegated_by field in context for traceability
Recommended Patterns
- Use a Message Broker or Task Queue (e.g., Redis, NATS, Kafka) to decouple coordination logic from tool execution
- Tag agents with capabilities and constraints in describe_model to support intelligent routing
- Build a context enrichment module to add insights, tags, or scoring to context between hops
By extending MCP into a multi-agent architecture, you unlock modular, reusable, and intelligently coordinated toolchains—enabling AI systems that are both scalable and adaptable. Whether you’re building RAG pipelines, smart assistants, or decision engines, this approach allows each component to act independently but still function as part of a larger cognitive system.
7. MCP with LangChain / LlamaIndex
LangChain and LlamaIndex have become go-to frameworks for building powerful LLM applications, thanks to their modularity and support for toolchains, retrieval-augmented generation (RAG), and agent orchestration.
MCP fits directly into these ecosystems by offering a clean, JSON-RPC-compatible way to expose external tools that agents can dynamically discover, describe, and invoke—exactly the kind of flexibility these frameworks are built for.
In this section, we’ll see how to plug MCP-powered tools into LangChain using their tool_call and tool_result APIs, and how to build a custom tool adapter that allows LangChain agents to work with MCP-based services as if they were native.
LangChain Custom Tools via MCP
LangChain lets you define tools that an agent can call as part of its reasoning loop. These tools expose:
- A name
- A description
- An invocation method (sync or async)
- A return format expected by the agent
You can create a custom MCPTool class in LangChain that wraps an MCP endpoint:
Use of tool_call and tool_result in OpenAI-Compatible Agents
LangChain also supports OpenAI-compatible tool use via the tool_call and tool_result mechanisms introduced in OpenAI’s function calling API.
MCP tools integrate into this pattern by:
- Accepting tool calls that match their describe_model method signature
- Returning structured results that conform to the expected output
For example, a tool call might look like:
Your LangChain agent can convert this into an MCP invoke call under the hood.
The returned tool result would be:
This works perfectly when paired with OpenAI-compatible agents like ChatOpenAI, OpenAIAgentExecutor, or even LangGraph for workflow orchestration.
Example: LangChain JSONTool + MCP Adapter
For more dynamic or context-rich use cases, you can subclass StructuredTool or use LangChain’s JSONTool to build a bridge to MCP:
You can dynamically switch tool calls at runtime, pass context between invocations, and plug the results back into the LangChain agent’s reasoning cycle.
This approach allows developers to integrate Model Context Protocol (MCP) without needing to rewrite existing LangChain-compatible tooling.
Why This Matters
By using MCP with LangChain or LlamaIndex, you’re:
- Decoupling logic – Tools evolve independently, but follow a shared protocol.
- Enabling dynamic discovery – Agents query describe_model instead of relying on static config.
- Standardizing invocation – Tools return predictable outputs in line with LangChain’s expectations.
- Supporting chained tool use – Context can be passed between MCP tool calls across the LangChain reasoning flow.
As more developers integrate Model Context Protocol (MCP) into AI systems, the need for standardization, context awareness, and intelligent orchestration becomes critical. Model Context Protocol (MCP) brings structure, clarity, and consistency to this evolution—allowing tools and models to interact through shared contracts, discover capabilities dynamically, and execute context-rich actions reliably.
In this guide, you’ve explored the MCP lifecycle end-to-end—from schema design and JSON-RPC server setup, to client-side invocation, context chaining, observability, security, and multi-agent coordination. You’ve also seen how MCP seamlessly integrates with modern frameworks like LangChain and LlamaIndex, enabling scalable, intelligent applications ready for real-world deployment.
If you’re building intelligent systems, now is the time to integrate Model Context Protocol (MCP) and standardize how your agents communicate.
Whether you’re building AI assistants, orchestration engines, or smart automation layers—MCP empowers your architecture to be not just connected, but contextually intelligent and future-ready.
Now it’s your move. Start building with MCP, and let your tools become true collaborators.