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Using mcp-use with Anthropic

The Anthropic adapter allows you to seamlessly integrate tools, resources, and prompts from any MCP server with the Anthropic Python SDK. This enables you to use mcp-use as a comprehensive tool provider for your Anthropic-powered agents.

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How it Works

The AnthropicMCPAdapter converts not only tools but also resources and prompts from your active MCP servers into a format compatible with Anthropic’s tool-calling feature. It maps each of these MCP constructs to a callable function that the Anthropic model can request.
  • Tools are converted directly to Anthropic functions.
  • Resources are converted into functions that take no arguments and read the resource’s content.
  • Prompts are converted into functions that accept the prompt’s arguments.
The adapter maintains a mapping of these generated functions to their actual execution logic, allowing you to easily call them when requested by the model.

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Step-by-Step Guide

Here’s how to use the adapter to provide MCP tools, resources, and prompts to an Anthropic Chat Completion.
Before starting, install the Anthropic SDK:
uv pip install anthropic
1
First, set up your MCPClient with the desired MCP servers. This part of the process is the same as any other mcp-use application.
from mcp_use import MCPClient

config = {
    "mcpServers": {
        "airbnb": {"command": "npx", "args": ["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"]},
    }
}

client = MCPClient(config=config)
2
Next, instantiate the AnthropicMCPAdapter. This adapter will be responsible for converting MCP constructs into a format Anthropic can understand.
from mcp_use.adapters import AnthropicMCPAdapter

# Creates the adapter for Anthropic's format
adapter = AnthropicMCPAdapter()
You can pass a disallowed_tools list to the adapter’s constructor to prevent specific tools, resources, or prompts from being exposed to the model.
3
Use the create_all method on the adapter to inspect all connected MCP servers and generate a list of tools, resources and prompts in the Anthropic function-calling format.
# Convert tools from active connectors to the Anthropic's format
# this will populates the list of tools, resources and prompts
await adapter.create_all(client)

# If you decided to create all tools (list concatenation)
anthropic_tools = adapter.tools + adapter.resources + adapter.prompts
This list will include functions generated from your MCP tools, resources, and prompts.
If you don’t want to create all tools, you can call single functions. For example, if you only want to use tools and resources, you can do the following:
await adapter.create_tools(client)
await adapter.create_resources(client)

# Then, you can decide which ones to use:
anthropic_tools = adapter.tools + adapter.resources
4
Now, you can use the generated anthropic_tools in a call to the Anthropic API. The model will use the descriptions of these tools to decide if it needs to call any of them to answer the user’s query.
from anthropic import Anthropic

anthropic = Anthropic()
messages = [
    {"role": "user", "content": "Please tell me the cheapest hotel for two people in Trapani."}
]

response = anthropic.messages.create(
    model="claude-3-opus-20240229",
    messages=messages,
    tools=anthropic_tools,
    max_tokens=1024
)

messages.append({"role": response.role, "content": response.content})
5
If the model decides to use one or more tools, the response.stop_reason will be tool_use. You need to iterate through the tool use content blocks, execute the corresponding functions, and append the results to your message history.The AnthropicMCPAdapter makes this easy by providing a tool_executors dictionary and a parse_result method.
if response.stop_reason == "tool_use":
    tool_results = []
    for c in response.content:
        if c.type != "tool_use":
            continue

        tool_name = c.name
        arguments = c.input

        # 1. Use the adapter's map to get the correct executor
        executor = adapter.tool_executors.get(tool_name)

        if not executor:
            content = f"Error: Tool '{tool_name}' not found."
        else:
            try:
                # 2. Execute the tool using the retrieved function
                print(f"Executing tool: {tool_name}({arguments})")
                tool_result = await executor(**arguments)

                # 3. Use the adapter's universal parser
                content = adapter.parse_result(tool_result)
            except Exception as e:
                content = f"Error executing tool: {e}"

        # 4. Append the result for this specific tool call
        tool_results.append(
            {
                "type": "tool_result",
                "tool_use_id": c.id,
                "content": content,
            }
        )
The adapter.parse_result(tool_result) method simplifies the process by correctly formatting the output, whether it’s from a standard tool, a resource, or a prompt.
6
Finally, send the updated message history which now includes the tool call results back to the model. This allows the model to use the information gathered from the tools to formulate its final answer.
if tool_results:
    messages.append(
        {
            "role": "user",
            "content": tool_results,
        }
    )
    # Get final response
    final_response = anthropic.messages.create(
        model="claude-3-opus-20240229", max_tokens=1024, tools=anthropic_tools, messages=messages
    )
    print("\n--- Final response from the model ---")
    print(final_response.content[0].text)

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Complete Example

For reference, here is the complete, runnable code for integrating mcp-use with the Anthropic SDK. 
import asyncio

from anthropic import Anthropic
from dotenv import load_dotenv

from mcp_use import MCPClient
from mcp_use.adapters import AnthropicMCPAdapter

# This example demonstrates how to use our integration
# adapters to use MCP tools and convert to the right format.
# In particularly, this example uses the AnthropicMCPAdapter.

load_dotenv()


async def main():
    config = {"mcpServers": {"server": {"url": "http://127.0.0.1:8080/mcp"}}}

    try:
        client = MCPClient(config=config)

        # Creates the adapter for Anthropic's format
        adapter = AnthropicMCPAdapter()

        # Convert tools from active connectors to the Anthropic's format
        await adapter.create_all(client)

        # List concatenation (if you loaded all tools)
        anthropic_tools = adapter.tools + adapter.resources + adapter.prompts

        # If you don't want to create all tools, you can call single functions
        # await adapter.create_tools(client)
        # await adapter.create_resources(client)
        # await adapter.create_prompts(client)

        # Use tools with Anthropic's SDK (not agent in this case)
        anthropic = Anthropic()

        # Initial request
        messages = [{"role": "user", "content": "Please could you give me the assistant prompt? My name is vincenzo"}]
        response = anthropic.messages.create(
            model="claude-3-opus-20240229", tools=anthropic_tools, max_tokens=1024, messages=messages
        )
        messages.append({"role": response.role, "content": response.content})

        print("Claude wants to use tools:", response.stop_reason == "tool_use")
        print("Number of tool calls:", len([c for c in response.content if c.type == "tool_use"]))

        if response.stop_reason == "tool_use":
            tool_results = []
            for c in response.content:
                if c.type != "tool_use":
                    continue

                tool_name = c.name
                arguments = c.input

                # Use the adapter's map to get the correct executor
                executor = adapter.tool_executors.get(tool_name)

                if not executor:
                    print(f"Error: Unknown tool '{tool_name}' requested by model.")
                    content = f"Error: Tool '{tool_name}' not found."
                else:
                    try:
                        # Execute the tool using the retrieved function
                        print(f"Executing tool: {tool_name}({arguments})")
                        tool_result = await executor(**arguments)

                        # Use the adapter's universal parser
                        content = adapter.parse_result(tool_result)
                    except Exception as e:
                        print(f"An unexpected error occurred while executing tool {tool_name}: {e}")
                        content = f"Error executing tool: {e}"

                # Append the result for this specific tool call
                tool_results.append(
                    {
                        "type": "tool_result",
                        "tool_use_id": c.id,
                        "content": content,
                    }
                )

            if tool_results:
                messages.append(
                    {
                        "role": "user",
                        "content": tool_results,
                    }
                )
                # Get final response
                final_response = anthropic.messages.create(
                    model="claude-3-opus-20240229", max_tokens=1024, tools=anthropic_tools, messages=messages
                )
                print("\n--- Final response from the model ---")
                print(final_response.content[0].text)
            else:
                final_response = response
                print("\n--- Final response from the model ---")
                if final_response.content:
                    print(final_response.content[0].text)

    except Exception as e:
        print(f"Error: {e}")
        raise e


if __name__ == "__main__":
    asyncio.run(main())