Model Context Protocol (MCP) Tools

The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models. Strands Agents integrates with MCP to extend agent capabilities through external tools and services.

MCP enables communication between agents and MCP servers that provide additional tools. Strands includes built-in support for connecting to MCP servers and using their tools in both Python and TypeScript.

Quick Start

=== “Python”

```python
from mcp import stdio_client, StdioServerParameters
from strands import Agent
from strands.tools.mcp import MCPClient

# Create MCP client with stdio transport
mcp_client = MCPClient(lambda: stdio_client(
    StdioServerParameters(
        command="uvx",
        args=["awslabs.aws-documentation-mcp-server@latest"]
    )
))

# Use with context manager for lifecycle management
with mcp_client:
    tools = mcp_client.list_tools_sync()
    agent = Agent(tools=tools)
    agent("What is AWS Lambda?")
```

=== “TypeScript”

```typescript
// Create MCP client with stdio transport
const mcpClient = new McpClient({
  transport: new StdioClientTransport({
    command: 'uvx',
    args: ['awslabs.aws-documentation-mcp-server@latest'],
  }),
})

// Pass MCP client directly to agent
const agent = new Agent({
  tools: [mcpClient],
})

await agent.invoke('What is AWS Lambda?')
```

Integration Approaches

=== “Python”

**Manual Context Management**

Python requires explicit context management using `with` statements to manage the MCP connection lifecycle:

```python
from mcp import stdio_client, StdioServerParameters
from strands import Agent
from strands.tools.mcp import MCPClient

mcp_client = MCPClient(lambda: stdio_client(
    StdioServerParameters(
        command="uvx",
        args=["awslabs.aws-documentation-mcp-server@latest"]
    )
))

# Manual lifecycle management
with mcp_client:
    tools = mcp_client.list_tools_sync()
    agent = Agent(tools=tools)
    agent("What is AWS Lambda?")  # Must be within context
```

This approach provides direct control over the MCP session lifecycle but requires careful management to avoid connection errors.

**Managed Integration (Experimental)**

!!! warning "Experimental Feature"
    The managed integration feature is experimental and may change in future versions. For production applications, use the manual context management approach.

The `MCPClient` implements the experimental `ToolProvider` interface, enabling direct usage in the Agent constructor with automatic lifecycle management:

```python
# Direct usage - connection lifecycle managed automatically
agent = Agent(tools=[mcp_client])
response = agent("What is AWS Lambda?")
```

=== “TypeScript”

**Direct Integration**

`McpClient` instances are passed directly to the agent. The client connects lazily on first use:

```typescript
const mcpClientDirect = new McpClient({
  transport: new StdioClientTransport({
    command: 'uvx',
    args: ['awslabs.aws-documentation-mcp-server@latest'],
  }),
})

// MCP client passed directly - connects on first tool use
const agentDirect = new Agent({
  tools: [mcpClientDirect],
})

await agentDirect.invoke('What is AWS Lambda?')
```

Tools can also be listed explicitly if needed:

```typescript
// Explicit tool listing
const tools = await mcpClient.listTools()
const agentExplicit = new Agent({ tools })
```

Transport Options

Both Python and TypeScript support multiple transport mechanisms for connecting to MCP servers.

Standard I/O (stdio)

For command-line tools and local processes that implement the MCP protocol:

=== “Python”

```python
from mcp import stdio_client, StdioServerParameters
from strands import Agent
from strands.tools.mcp import MCPClient

# For macOS/Linux:
stdio_mcp_client = MCPClient(lambda: stdio_client(
    StdioServerParameters(
        command="uvx",
        args=["awslabs.aws-documentation-mcp-server@latest"]
    )
))

# For Windows:
stdio_mcp_client = MCPClient(lambda: stdio_client(
    StdioServerParameters(
        command="uvx",
        args=[
            "--from",
            "awslabs.aws-documentation-mcp-server@latest",
            "awslabs.aws-documentation-mcp-server.exe"
        ]
    )
))

with stdio_mcp_client:
    tools = stdio_mcp_client.list_tools_sync()
    agent = Agent(tools=tools)
    response = agent("What is AWS Lambda?")
```

=== “TypeScript”

```typescript
const stdioClient = new McpClient({
  transport: new StdioClientTransport({
    command: 'uvx',
    args: ['awslabs.aws-documentation-mcp-server@latest'],
  }),
})

const agentStdio = new Agent({
  tools: [stdioClient],
})

await agentStdio.invoke('What is AWS Lambda?')
```

Streamable HTTP

For HTTP-based MCP servers that use Streamable HTTP transport:

=== “Python”

```python
from mcp.client.streamable_http import streamablehttp_client
from strands import Agent
from strands.tools.mcp import MCPClient

streamable_http_mcp_client = MCPClient(
    lambda: streamablehttp_client("http://localhost:8000/mcp")
)

with streamable_http_mcp_client:
    tools = streamable_http_mcp_client.list_tools_sync()
    agent = Agent(tools=tools)
```

Additional properties like authentication can be configured:

```python
import os
from mcp.client.streamable_http import streamablehttp_client
from strands.tools.mcp import MCPClient

github_mcp_client = MCPClient(
    lambda: streamablehttp_client(
        url="https://api.githubcopilot.com/mcp/",
        headers={"Authorization": f"Bearer {os.getenv('MCP_PAT')}"}
    )
)
```

#### AWS IAM

For MCP servers on AWS that use SigV4 authentication with IAM credentials, you can conveniently use the [`mcp-proxy-for-aws`](https://pypi.org/project/mcp-proxy-for-aws/) package to handle AWS credential management and request signing automatically. See the [detailed guide](https://dev.to/aws/no-oauth-required-an-mcp-client-for-aws-iam-k1o) for more information.

First, install the package:

```bash
pip install mcp-proxy-for-aws
```

Then you use it like any other transport:

```python
from mcp_proxy_for_aws.client import aws_iam_streamablehttp_client
from strands.tools.mcp import MCPClient

mcp_client = MCPClient(lambda: aws_iam_streamablehttp_client(
    endpoint="https://your-service.us-east-1.amazonaws.com/mcp",
    aws_region="us-east-1",
    aws_service="bedrock-agentcore"
))
```

=== “TypeScript”

```typescript
const httpClient = new McpClient({
  transport: new StreamableHTTPClientTransport(
    new URL('http://localhost:8000/mcp')
  ) as Transport,
})

const agentHttp = new Agent({
  tools: [httpClient],
})

// With authentication
const githubMcpClient = new McpClient({
  transport: new StreamableHTTPClientTransport(
    new URL('https://api.githubcopilot.com/mcp/'),
    {
      requestInit: {
        headers: {
          Authorization: `Bearer ${process.env.GITHUB_PAT}`,
        },
      },
    }
  ) as Transport,
})
```

Server-Sent Events (SSE)

=== “Python”

For HTTP-based MCP servers that use Server-Sent Events transport:

```python
from mcp.client.sse import sse_client
from strands import Agent
from strands.tools.mcp import MCPClient

sse_mcp_client = MCPClient(lambda: sse_client("http://localhost:8000/sse"))

with sse_mcp_client:
    tools = sse_mcp_client.list_tools_sync()
    agent = Agent(tools=tools)
```

=== “TypeScript”

```typescript
import { SSEClientTransport } from '@modelcontextprotocol/sdk/client/sse.js'

const sseClient = new McpClient({
  transport: new SSEClientTransport(
    new URL('http://localhost:8000/sse')
  ),
})

const agentSse = new Agent({
  tools: [sseClient],
})
```

Using Multiple MCP Servers

Combine tools from multiple MCP servers in a single agent:

=== “Python”

```python
from mcp import stdio_client, StdioServerParameters
from mcp.client.sse import sse_client
from strands import Agent
from strands.tools.mcp import MCPClient

# Create multiple clients
sse_mcp_client = MCPClient(lambda: sse_client("http://localhost:8000/sse"))
stdio_mcp_client = MCPClient(lambda: stdio_client(
    StdioServerParameters(command="python", args=["path/to/mcp_server.py"])
))

# Manual approach - explicit context management
with sse_mcp_client, stdio_mcp_client:
    tools = sse_mcp_client.list_tools_sync() + stdio_mcp_client.list_tools_sync()
    agent = Agent(tools=tools)

# Managed approach (experimental)
agent = Agent(tools=[sse_mcp_client, stdio_mcp_client])
```

=== “TypeScript”

```typescript
const localClient = new McpClient({
  transport: new StdioClientTransport({
    command: 'uvx',
    args: ['awslabs.aws-documentation-mcp-server@latest'],
  }),
})

const remoteClient = new McpClient({
  transport: new StreamableHTTPClientTransport(
    new URL('https://api.example.com/mcp/')
  ) as Transport,
})

// Pass multiple MCP clients to the agent
const agentMultiple = new Agent({
  tools: [localClient, remoteClient],
})
```

Client Configuration

=== “Python”

Python's `MCPClient` supports tool filtering and name prefixing to manage tools from multiple servers.

**Tool Filtering**

Control which tools are loaded using the `tool_filters` parameter:

```python
from mcp import stdio_client, StdioServerParameters
from strands.tools.mcp import MCPClient
import re

# String matching - loads only specified tools
filtered_client = MCPClient(
    lambda: stdio_client(StdioServerParameters(
        command="uvx",
        args=["awslabs.aws-documentation-mcp-server@latest"]
    )),
    tool_filters={"allowed": ["search_documentation", "read_documentation"]}
)

# Regex patterns
regex_client = MCPClient(
    lambda: stdio_client(StdioServerParameters(
        command="uvx",
        args=["awslabs.aws-documentation-mcp-server@latest"]
    )),
    tool_filters={"allowed": [re.compile(r"^search_.*")]}
)

# Combined filters - applies allowed first, then rejected
combined_client = MCPClient(
    lambda: stdio_client(StdioServerParameters(
        command="uvx",
        args=["awslabs.aws-documentation-mcp-server@latest"]
    )),
    tool_filters={
        "allowed": [re.compile(r".*documentation$")],
        "rejected": ["read_documentation"]
    }
)
```

**Tool Name Prefixing**

Prevent name conflicts when using multiple MCP servers:

```python
aws_docs_client = MCPClient(
    lambda: stdio_client(StdioServerParameters(
        command="uvx",
        args=["awslabs.aws-documentation-mcp-server@latest"]
    )),
    prefix="aws_docs"
)

other_client = MCPClient(
    lambda: stdio_client(StdioServerParameters(
        command="uvx",
        args=["other-mcp-server@latest"]
    )),
    prefix="other"
)

# Tools will be named: aws_docs_search_documentation, other_search, etc.
agent = Agent(tools=[aws_docs_client, other_client])
```

=== “TypeScript”

TypeScript's `McpClient` accepts optional application metadata:

```typescript
const mcpClient = new McpClient({
  applicationName: 'My Agent App',
  applicationVersion: '1.0.0',
  transport: new StdioClientTransport({
    command: 'npx',
    args: ['-y', 'some-mcp-server'],
  }),
})
```

Tool filtering and prefixing are not currently supported in TypeScript.

Direct Tool Invocation

While tools are typically invoked by the agent based on user requests, MCP tools can also be called directly:

=== “Python”

```python
result = mcp_client.call_tool_sync(
    tool_use_id="tool-123",
    name="calculator",
    arguments={"x": 10, "y": 20}
)
print(f"Result: {result['content'][0]['text']}")
```

=== “TypeScript”

```typescript
// Get tools and find the target tool
const tools = await mcpClient.listTools()
const calcTool = tools.find(t => t.name === 'calculator')

// Call directly through the client
const result = await mcpClient.callTool(calcTool, { x: 10, y: 20 })
```

Implementing an MCP Server

Custom MCP servers can be created to extend agent capabilities:

=== “Python”

```python
from mcp.server import FastMCP

# Create an MCP server
mcp = FastMCP("Calculator Server")

# Define a tool
@mcp.tool(description="Calculator tool which performs calculations")
def calculator(x: int, y: int) -> int:
    return x + y

# Run the server with SSE transport
mcp.run(transport="sse")
```

=== “TypeScript”

```typescript
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'
import { z } from 'zod'

const server = new McpServer({
  name: 'Calculator Server',
  version: '1.0.0',
})

server.tool(
  'calculator',
  'Calculator tool which performs calculations',
  {
    x: z.number(),
    y: z.number(),
  },
  async ({ x, y }) => {
    return {
      content: [{ type: 'text', text: String(x + y) }],
    }
  }
)

const transport = new StdioServerTransport()
await server.connect(transport)
```

For more information on implementing MCP servers, see the MCP documentation.

Advanced Usage

=== “Python”

### Elicitation

An MCP server can request additional information from the user by sending an elicitation request. Set up an elicitation callback to handle these requests:

```python
# server.py
from mcp.server import FastMCP
from pydantic import BaseModel, Field

class ApprovalSchema(BaseModel):
    username: str = Field(description="Who is approving?")

server = FastMCP("mytools")

@server.tool()
async def delete_files(paths: list[str]) -> str:
    result = await server.get_context().elicit(
        message=f"Do you want to delete {paths}",
        schema=ApprovalSchema,
    )
    if result.action != "accept":
        return f"User {result.data.username} rejected deletion"

    # Perform deletion...
    return f"User {result.data.username} approved deletion"

server.run()
```

```python
# client.py
from mcp import stdio_client, StdioServerParameters
from mcp.types import ElicitResult
from strands import Agent
from strands.tools.mcp import MCPClient

async def elicitation_callback(context, params):
    print(f"ELICITATION: {params.message}")
    # Get user confirmation...
    return ElicitResult(
        action="accept",
        content={"username": "myname"}
    )

client = MCPClient(
    lambda: stdio_client(
        StdioServerParameters(command="python", args=["/path/to/server.py"])
    ),
    elicitation_callback=elicitation_callback,
)

with client:
    agent = Agent(tools=client.list_tools_sync())
    result = agent("Delete 'a/b/c.txt' and share the name of the approver")
```

For more information on elicitation, see the [MCP specification](https://modelcontextprotocol.io/specification/draft/client/elicitation).

{{ ts_not_supported_code() }}

Best Practices

• Tool Descriptions: Provide clear descriptions for tools to help the agent understand when and how to use them
• Error Handling: Return informative error messages when tools fail to execute properly
• Security: Consider security implications when exposing tools via MCP, especially for network-accessible servers
• Connection Management: In Python, always use context managers (with statements) to ensure proper cleanup of MCP connections
• Timeouts: Set appropriate timeouts for tool calls to prevent hanging on long-running operations

Troubleshooting

MCPClientInitializationError (Python)

Tools relying on an MCP connection must be used within a context manager. Operations will fail when the agent is used outside the with statement block.

# Correct
with mcp_client:
    agent = Agent(tools=mcp_client.list_tools_sync())
    response = agent("Your prompt")  # Works

# Incorrect
with mcp_client:
    agent = Agent(tools=mcp_client.list_tools_sync())
response = agent("Your prompt")  # Fails - outside context

Connection Failures

Connection failures occur when there are problems establishing a connection with the MCP server. Verify that:

• The MCP server is running and accessible
• Network connectivity is available and firewalls allow the connection
• The URL or command is correct and properly formatted

Tool Discovery Issues

If tools aren’t being discovered:

• Confirm the MCP server implements the list_tools method correctly
• Verify all tools are registered with the server

Tool Execution Errors

When tool execution fails:

• Verify tool arguments match the expected schema
• Check server logs for detailed error information