How to Use MCP in Claude

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As large language models (LLMs) become central to applications across industries, developers are seeking more modular and context-aware solutions. One emerging solution is Model Context Protocol (MCP)—a protocol that orchestrates memory, tools, and agent routing in LLM-based systems. But how can this be applied with Anthropic’s Claude models?

In this guide, we’ll walk through how to use MCP in Claude, covering architecture, best practices, deployment strategies, and the technical details you need to know to build scalable, context-rich applications using Claude and MCP.

What Is MCP (Model Context Protocol)?

MCP is a framework that enables multi-agent and memory-enhanced LLM applications. It defines a common protocol for routing requests, maintaining long-term memory, integrating tools, and coordinating agents. Key features include:

  • Session-based memory handling
  • Tool invocation routing
  • Multi-agent coordination (task planners, retrievers, executors)
  • Extensible communication between components

MCP allows developers to build smarter, modular systems that can:

  • Remember prior user input
  • Dynamically switch between tools (e.g., search, calculator, web)
  • Split complex tasks into sub-agents

What Is Claude?

Claude is Anthropic’s conversational LLM family, known for:

  • Ethical and safe responses
  • Long context windows (up to 200K tokens in Claude 3 Opus)
  • Fast and scalable inference (Claude 3 Haiku, Sonnet)
  • Strong reasoning and reading comprehension performance

Claude models are accessible via:

  • Claude’s web interface (claude.ai)
  • The Anthropic API
  • Third-party integrations (Notion, Slack, etc.)

To use Claude within MCP, you’ll typically integrate via the Anthropic API, allowing Claude to serve as an LLM node in your MCP graph.

Why Combine MCP with Claude?

Integrating Claude into an MCP pipeline unlocks powerful capabilities for building intelligent and modular LLM-based systems. By combining Claude’s natural language generation with the routing, memory, and multi-agent support offered by MCP, developers can create more sophisticated applications. Claude’s ability to handle large context windows complements MCP’s session and memory-based architecture, allowing systems to retain history across conversations and deliver more contextually relevant responses. Additionally, Claude’s reliable performance and cost-efficiency make it a practical choice for deploying at scale.

This integration also enables tool augmentation: Claude can be orchestrated alongside APIs, retrieval tools, or calculators to answer complex queries or automate workflows. Developers can fine-tune routing logic within MCP to dynamically determine whether to pass tasks to Claude or to other agents, depending on the user’s intent. Overall, the synergy between MCP’s orchestration capabilities and Claude’s strong reasoning makes this combination ideal for production-grade, multi-functional AI systems.

Architecture Overview: MCP with Claude

An example architecture:

[User] → [MCP Router] → [Claude Agent]
                       ↘ [Memory Agent (Redis)]
                       ↘ [Search Agent]
                       ↘ [Math Tool Agent]

Each component is containerized and communicates via shared schemas (e.g., JSON-based requests/responses).

Claude Agent

  • Wraps the Claude API
  • Handles prompt formatting
  • Accepts tasks from router
  • Sends back generated responses

MCP Router

  • Interprets user intent
  • Routes to Claude or tool agents
  • Manages session ID and history context

Tool Agents

  • Simple APIs (e.g., /search?q=... or /math?expr=...)
  • Return structured output to the router

How to Set Up MCP for Claude

1. Get Access to the Anthropic API

Sign up at https://console.anthropic.com and generate an API key. Claude supports the following models:

  • claude-3-opus-20240229
  • claude-3-sonnet-20240229
  • claude-3-haiku-20240307

2. Install Required Libraries

Use Python and HTTP clients like requests or httpx:

pip install requests

3. Write the Claude Agent Wrapper

Here’s a minimal Claude agent function:

import requests
import os

CLAUDE_API_KEY = os.getenv("ANTHROPIC_API_KEY")

def call_claude(prompt):
    url = "https://api.anthropic.com/v1/messages"
    headers = {
        "x-api-key": CLAUDE_API_KEY,
        "anthropic-version": "2023-06-01",
        "content-type": "application/json"
    }
    data = {
        "model": "claude-3-sonnet-20240229",
        "max_tokens": 1024,
        "messages": [
            {"role": "user", "content": prompt}
        ]
    }
    response = requests.post(url, headers=headers, json=data)
    return response.json()["content"]

Wrap this in a FastAPI service or Flask route to make it callable from MCP.

4. Create the MCP Router

Your router:

  • Accepts user input
  • Checks for tool triggers
  • Formats prompt for Claude
  • Sends to Claude or a sub-agent
  • Aggregates responses

Pseudocode:

if "calculate" in user_input:
    result = call_math_tool(user_input)
    prompt = f"A user asked: '{user_input}'. I got this result: {result}. Summarize it."
    return call_claude(prompt)
else:
    return call_claude(user_input)

5. Add Memory or Session Context

Use Redis or a database to store previous messages. Retrieve recent history and append to Claude’s input:

context = retrieve_session(user_id)
prompt = f"Context: {context}\nUser: {user_input}\nAssistant:"

Claude handles long context windows well, but you should still truncate or summarize history to stay within token limits.

Example Use Case: Claude + Tools + Memory

User Input: “What is the average rainfall in Tokyo in March, and could you also calculate how much that is in gallons per square meter?”

System Flow:

  • Router detects need for search and math
  • Search Agent → Gets rainfall in mm
  • Math Agent → Converts mm to gallons/m^2
  • Claude → Summarizes all in natural language

Final Output:

“In March, Tokyo receives around 117 mm of rainfall. That’s approximately 30.9 gallons per square meter.”

This kind of chaining is exactly what MCP + Claude enables.

Best Practices

  • Use Claude’s system prompts to guide tone and behavior. These prompts help control Claude’s responses to align with the assistant’s intended persona and reduce ambiguity.
  • Cache Claude responses to reduce cost and latency. Implement caching layers for repeat queries, especially in use cases like FAQs or recurring workflows.
  • Limit tool calls per session for latency control. Repeated or excessive external API calls can slow down the user experience; consider batching tool requests where possible.
  • Log all routing decisions for debugging and transparency. This helps diagnose failures and optimize routing logic over time.
  • Store session context with expiration logic. Retaining relevant user interaction history enhances continuity, but it’s important to manage memory size and token limits.
  • Regularly audit and update prompt templates and agent behavior to align with evolving requirements.
  • Test Claude behavior with both normal and edge-case inputs to ensure consistent performance across various scenarios.

Security Considerations

  • Never expose Claude API keys in frontend code
  • Rate-limit external tool calls
  • Sanitize user input passed to Claude or tools
  • Monitor logs for abuse or misuse

Future Potential

Claude + MCP opens doors to:

  • Multi-agent AI assistants with specialized roles
  • Domain-specific knowledge systems (legal, health, finance)
  • Long-running research assistants with memory and reasoning
  • Conversational agents that use APIs and documents

Conclusion

Using MCP with Claude unlocks modular, context-aware, and tool-augmented LLM workflows. Whether you’re building research agents, assistants, or autonomous pipelines, Claude’s safety, long context window, and powerful reasoning abilities make it an ideal candidate for MCP integration.

By orchestrating tools, routing logic, and memory layers through a unified protocol, developers can go beyond basic prompts and build intelligent systems that truly understand and assist users at scale.

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