MCP Client Call Reference Documentation¶

This document provides a comprehensive reference for the MCP (Model Control Protocol) client call functions, including detailed parameter descriptions, return types, and usage examples.

Table of Contents¶

aget_mcp_tools
get_mcp_tools_sync
get_tools_for_multiple_mcp_servers
execute_tool_call_simple

Function Reference¶

aget_mcp_tools¶

Asynchronously fetches available MCP tools from the server with retry logic.

Parameters¶

Parameter	Type	Required	Description
server_path	Optional[str]	No	Path to the MCP server script
format	str	No	Format of the returned tools (default: "openai")
connection	Optional[MCPConnection]	No	MCP connection object
*args	Any	No	Additional positional arguments
**kwargs	Any	No	Additional keyword arguments

Returns¶

List[Dict[str, Any]]: List of available MCP tools in OpenAI format

Raises¶

MCPValidationError: If server_path is invalid
MCPConnectionError: If connection to server fails

Example¶

import asyncio
from swarms.tools.mcp_client_tools import aget_mcp_tools
from swarms.tools.mcp_connection import MCPConnection

async def main():
    # Using server path
    tools = await aget_mcp_tools(server_path="http://localhost:8000")

    # Using connection object
    connection = MCPConnection(
        host="localhost",
        port=8000,
        headers={"Authorization": "Bearer token"}
    )
    tools = await aget_mcp_tools(connection=connection)

    print(f"Found {len(tools)} tools")

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

get_mcp_tools_sync¶

Synchronous version of get_mcp_tools that handles event loop management.

Parameters¶

Parameter	Type	Required	Description
server_path	Optional[str]	No	Path to the MCP server script
format	str	No	Format of the returned tools (default: "openai")
connection	Optional[MCPConnection]	No	MCP connection object
*args	Any	No	Additional positional arguments
**kwargs	Any	No	Additional keyword arguments

Returns¶

List[Dict[str, Any]]: List of available MCP tools in OpenAI format

Raises¶

MCPValidationError: If server_path is invalid
MCPConnectionError: If connection to server fails
MCPExecutionError: If event loop management fails

Example¶

from swarms.tools.mcp_client_tools import get_mcp_tools_sync
from swarms.tools.mcp_connection import MCPConnection

# Using server path
tools = get_mcp_tools_sync(server_path="http://localhost:8000")

# Using connection object
connection = MCPConnection(
    host="localhost",
    port=8000,
    headers={"Authorization": "Bearer token"}
)
tools = get_mcp_tools_sync(connection=connection)

print(f"Found {len(tools)} tools")

get_tools_for_multiple_mcp_servers¶

Get tools for multiple MCP servers concurrently using ThreadPoolExecutor.

Parameters¶

Parameter	Type	Required	Description
urls	List[str]	Yes	List of server URLs to fetch tools from
connections	List[MCPConnection]	No	Optional list of MCPConnection objects
format	str	No	Format to return tools in (default: "openai")
output_type	Literal["json", "dict", "str"]	No	Type of output format (default: "str")
max_workers	Optional[int]	No	Maximum number of worker threads

Returns¶

List[Dict[str, Any]]: Combined list of tools from all servers

Raises¶

MCPExecutionError: If fetching tools from any server fails

Example¶

from swarms.tools.mcp_client_tools import get_tools_for_multiple_mcp_servers
from swarms.tools.mcp_connection import MCPConnection

# Define server URLs
urls = [
    "http://server1:8000",
    "http://server2:8000"
]

# Optional: Define connections
connections = [
    MCPConnection(host="server1", port=8000),
    MCPConnection(host="server2", port=8000)
]

# Get tools from all servers
tools = get_tools_for_multiple_mcp_servers(
    urls=urls,
    connections=connections,
    format="openai",
    output_type="dict",
    max_workers=4
)

print(f"Found {len(tools)} tools across all servers")

execute_tool_call_simple¶

Execute a tool call using the MCP client.

Parameters¶

Parameter	Type	Required	Description
response	Any	No	Tool call response object
server_path	str	No	Path to the MCP server
connection	Optional[MCPConnection]	No	MCP connection object
output_type	Literal["json", "dict", "str", "formatted"]	No	Type of output format (default: "str")
*args	Any	No	Additional positional arguments
**kwargs	Any	No	Additional keyword arguments

Returns¶

List[Dict[str, Any]]: Result of the tool execution

Raises¶

MCPConnectionError: If connection to server fails
MCPExecutionError: If tool execution fails

Example¶

import asyncio
from swarms.tools.mcp_client_tools import execute_tool_call_simple
from swarms.tools.mcp_connection import MCPConnection

async def main():
    # Example tool call response
    response = {
        "name": "example_tool",
        "parameters": {"param1": "value1"}
    }

    # Using server path
    result = await execute_tool_call_simple(
        response=response,
        server_path="http://localhost:8000",
        output_type="json"
    )

    # Using connection object
    connection = MCPConnection(
        host="localhost",
        port=8000,
        headers={"Authorization": "Bearer token"}
    )
    result = await execute_tool_call_simple(
        response=response,
        connection=connection,
        output_type="dict"
    )

    print(f"Tool execution result: {result}")

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

Error Handling¶

The MCP client functions use a retry mechanism with exponential backoff for failed requests. The following error types may be raised:

MCPValidationError: Raised when input validation fails
MCPConnectionError: Raised when connection to the MCP server fails
MCPExecutionError: Raised when tool execution fails

Best Practices¶

Always handle potential exceptions when using these functions
Use connection objects for authenticated requests
Consider using the async versions for better performance in async applications
Use appropriate output types based on your needs
When working with multiple servers, adjust max_workers based on your system's capabilities