How To Use Claude MCP

Claude MCP (Model Context Protocol) is an open protocol that lets Claude AI interact with external tools and data sources. Think of it like a USB port for AI - it provides a standardized way to connect Claude to different systems.

Without MCP, Claude can only respond based on its training. With MCP, Claude can read files from your computer, search the web, query databases, or use any custom tool you create. The protocol defines how Claude sends requests to tools and how those tools respond.

MCP works through a client-server model. Claude is the client that sends requests, while MCP servers provide the tools Claude can use. These tools are defined using JSON Schema, which tells Claude what inputs the tool accepts and what outputs it returns.

• Connection framework: MCP creates a bridge between Claude and external systems

• Tool-based design: Each capability is exposed as a discrete tool with clear inputs and outputs

• Standard protocol: Uses JSON Schema to define consistent interfaces

Why integrate Claude with MCP servers

Claude without MCP is limited to conversation. It can't access your files, check real-time information, or perform actions on your behalf. Adding MCP servers dramatically expands what Claude can do.

When you connect Claude to MCP servers, it can read and write files, search the web, query databases, and use custom tools you create. This makes Claude much more useful for real-world tasks that require access to your data or systems.

For example, with MCP servers, Claude can:

• Read a document from your computer and summarize it

• Save a generated image to a specific folder

• Look up information from a database

• Execute commands in your development environment

This integration turns Claude from just a conversational AI into a practical assistant that can interact with your digital world.

Setting up Claude AI MCP

Setting up Claude MCP involves connecting Claude to MCP servers that provide tools. The most common way to do this is through Claude Desktop, which supports MCP configuration.

1. Requirements

Before starting, make sure you have:

• Claude Desktop installed (available for macOS and Windows)

• Node.js installed (required for many MCP servers)

• Basic understanding of JSON configuration files

• Appropriate permissions for any file system access

You can check if Node.js is installed by opening a terminal or command prompt and typing node --version. If it's not installed, download it from nodejs.org.

2. Basic configuration steps

Claude Desktop uses a JSON configuration file to know which MCP servers to connect to. Here's how to set it up:

1. Open Claude Desktop

2. Click on the Claude menu and select "Settings..."

3. Click on "Developer" in the left sidebar

4. Click "Edit Config" to open the configuration file

This will open or create a file called claude_desktop_config.json in one of these locations:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

Add a basic file system MCP server by replacing the file contents with:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/username/Desktop",
        "/Users/username/Downloads"
      ]
    }
  }
}

Replace "username" with your actual username and adjust the paths to match directories you want Claude to access.

3. Testing your setup

After saving the configuration file, restart Claude Desktop. You should see a tools icon in the bottom left corner of the chat input area. This indicates that MCP servers are connected.

To test if it's working:

• Ask Claude to list files in one of your configured directories

• Request Claude to create a text file in one of those directories

• Check if Claude asks for permission before performing file operations

If you don't see the tools icon or Claude can't access your files, check:

• That the paths in your configuration file are correct and accessible

• That Node.js is properly installed

• The Claude logs for errors (found in ~/Library/Logs/Claude/ on macOS or %APPDATA%\Claude\logs\ on Windows)

Steps to configure Claude MCP server

Creating your own MCP server lets you define custom tools for Claude to use. This section covers how to set up a basic server from scratch.

1. Installing dependencies

First, install the necessary software:

# Install Node.js if you haven't already
# Then install the MCP server SDK
npm

For Python-based servers, you can use:

These SDKs provide frameworks for creating MCP-compatible servers that Claude can connect to.

2. Server configuration file

A basic MCP server needs:

• Tool definitions that specify what the tools do

• A configuration file that tells Claude how to connect

Here's a simple example of a server configuration in claude_desktop_config.json:

{
  "mcpServers": {
    "weather": {
      "command": "node",
      "args": ["weather-server.js"],
      "env": {
        "API_KEY": "your-weather-api-key"
      }
    }
  }
}

This tells Claude to run a Node.js script called weather-server.js and pass it an environment variable with an API key.

3. Verifying MCP server status

After configuring your server, check if it's running correctly:

1. Restart Claude Desktop

2. Type /mcp in the chat to see active servers and their status

3. Look for your server in the list and check if it shows as "connected"

If your server isn't connecting, check:

• The server logs for error messages

• That your server is running the correct protocol version

• That all paths and commands are correct

Common issues include missing dependencies, incorrect file paths, or permission problems.

How to manage large APIs with Claude MCP

When working with large APIs through MCP, you'll face challenges with schema size and complexity. Here's how to handle them effectively.

1. Schema organization

Large APIs often have hundreds of endpoints, each becoming a tool in MCP. To manage this complexity:

• Group related endpoints under consistent naming patterns

• Use clear, descriptive tool names that Claude can easily understand

• Provide detailed descriptions for each tool to help Claude select the right one

For example, instead of generic names like "getUser" and "getProduct", use more specific names like "users_getById" and "products_searchByCategory".

2. Pagination and boundary checks

When APIs return large datasets, implement pagination in your MCP tools:

• Add pagination parameters (limit, offset, page) to your tool schemas

• Set reasonable defaults and limits to prevent excessive data retrieval

• Include clear error handling for when limits are exceeded

This prevents Claude from requesting too much data at once, which could cause timeouts or rate limiting issues.

3. Handling bulk endpoints

For APIs with many endpoints, consider these approaches:

• Create specialized MCP servers for different parts of your API

• Implement dynamic loading of tools based on the current conversation context

• Use tool categories or tags to help Claude navigate large tool collections

Claude can connect to multiple MCP servers simultaneously, so splitting functionality across servers can improve organization and performance.

Building a custom Claude MCP implementation

Creating your own MCP tools gives you complete control over what Claude can do. Here's how to build custom implementations.

1. Defining custom tools

Each tool needs a schema definition and an implementation function. Here's a simple example in JavaScript:

import { defineTool } from "@modelcontextprotocol/server";

export default defineTool({
  name: "getWeather",
  description: "Gets the current weather for a location",
  inputSchema: {
    type: "object",
    properties: {
      location: {
        type: "string",
        description: "City name or zip code"
      }
    },
    required: ["location"]
  },
  execute: async ({ location }) => {
    // Fetch weather data from an API
    const response = await fetch(`https://api.weather.com?location=${location}`);
    const data = await response.json();
    return {
      temperature: data.temp,
      conditions: data.conditions
    };
  }
});

This defines a tool that takes a location as input, fetches weather data, and returns temperature and conditions.

2. Adding authentication logic

For tools that access protected resources, add authentication:

• Use environment variables for API keys and tokens

• Implement OAuth flows for more secure authentication

• Add permission checks before executing sensitive operations

Claude MCP supports OAuth authentication, which can be initiated using the /mcp command in Claude Desktop.

3. Publishing your implementation

To share your MCP implementation:

• Package it as an npm module or Python package

• Include clear documentation on how to use and configure it

• Add examples of how to connect it to Claude

You can publish to npm or PyPI, or share the code directly through GitHub or other repositories.

Advanced tips for Claude memory MCP

Claude memory MCP allows for persistent context across conversations. Here's how to use it effectively.

1. Storing and reusing context

MCP servers can maintain state between conversations by:

• Storing data in files or databases

• Creating memory tools that save and retrieve information

• Implementing session management to keep context per user

This lets Claude remember information from previous conversations or tool calls, making interactions more coherent over time.

2. Optimizing performance

To keep Claude MCP running smoothly:

• Limit the size of data returned from tools

• Use caching for frequently accessed information

• Break complex operations into smaller, more focused tools

Large responses or slow tools can cause timeouts, so optimize for speed and efficiency.

3. Common pitfalls

Watch out for these common issues:

• Circular references in tool calls leading to infinite loops

• Timeout errors from slow or unresponsive tools

• Schema validation failures from mismatched data types

• Permission issues when accessing protected resources

Regular testing and monitoring helps catch these problems early.

MCP Servers with Stainless

At Stainless, we've been working on solutions to transform OpenAPI specifications into MCP servers.

Our platform generates high-quality SDKs from OpenAPI specs, and we're applying that same expertise to MCP server generation. This makes it easier to expose your API as tools that Claude can use, with all the type safety and validation you'd expect.

If you're looking to create an MCP server for your API, check out our platform at https://app.stainless.com/signup to get started for free.

FAQs about Claude MCP

How can I secure Claude MCP for enterprise environments?

Secure Claude MCP by implementing proper authentication (OAuth or API keys), using HTTPS for all connections, restricting file system access to specific directories, and implementing permission checks for sensitive operations. Review all tool implementations for security vulnerabilities before deployment.

Does Claude memory MCP retain user data indefinitely?

No, Claude memory MCP retains data according to the configuration of your MCP server. You can set data to expire after a certain time, be deleted when sessions end, or implement custom retention policies. The server controls how long data is stored.

What is the difference between Claude MCP and regular Claude AI?

Regular Claude AI can only have conversations based on its training data. Claude MCP can interact with external systems through tools, allowing it to read files, access APIs, and perform actions in the real world. This makes Claude MCP much more versatile for practical tasks.

Can MCP servers Claude connect to multiple tools simultaneously?

Yes, Claude can connect to multiple MCP servers at once, each providing different tools. Within a single conversation, Claude can use any available tool from any connected server. This allows for complex workflows that combine different capabilities.