mcp_run POC
This commit is contained in:
vipyne
@@ -4,13 +4,11 @@
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import aiohttp
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import asyncio
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import os
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import sys
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import json
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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@@ -22,135 +20,20 @@ from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.anthropic.llm import AnthropicLLMService
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.services.mcp_run.mcp_run import MCPRun
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from pipecat.services.anthropic.llm import AnthropicLLMService
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from pipecat.services.google.llm import GoogleLLMService
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from pipecat.services.openai.llm import OpenAILLMService
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from mcp_run import Client
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load_dotenv(override=True)
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logger.remove()
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logger.add(sys.stderr, level="DEBUG")
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def convert_mcp_schema_to_pipecat(tool_name: str, tool_schema: dict[str, any]) -> FunctionSchema:
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"""Convert an mcp.run tool schema to Pipecat's FunctionSchema format.
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Args:
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tool_name: The name of the tool
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tool_schema: The mcp.run tool schema
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Returns:
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A FunctionSchema instance
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"""
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logger.debug(f"Converting schema for tool '{tool_name}'")
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logger.debug(f"Original schema: {json.dumps(tool_schema, indent=2)}")
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# Extract properties and required fields from the mcp.run schema
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properties = tool_schema["input_schema"].get("properties", {})
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required = tool_schema["input_schema"].get("required", [])
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schema = FunctionSchema(
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name=tool_name,
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description=tool_schema["description"],
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properties=properties,
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required=required
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)
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logger.debug(f"Converted schema: {json.dumps(schema.to_default_dict(), indent=2)}")
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return schema
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async def mcp_tool_wrapper(function_name: str, tool_call_id: str, arguments: dict[str, any],
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llm: any, context: any, result_callback: any) -> None:
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"""Wrapper function for mcp.run tool calls that matches Pipecat's function call interface.
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Args:
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function_name: Name of the tool to call
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tool_call_id: Unique ID for this tool call
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arguments: Tool parameters
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llm: LLM service instance
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context: Context object
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result_callback: Callback function to return results
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"""
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logger.debug(f"Executing tool '{function_name}' with call ID: {tool_call_id}")
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logger.debug(f"Tool arguments: {json.dumps(arguments, indent=2)}")
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try:
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# Call the mcp.run tool
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logger.debug(f"Calling mcp.run tool '{function_name}'")
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results = llm.mcp_client.call_tool(function_name, params=arguments)
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# Combine all content into a single response
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response = ""
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for i, content in enumerate(results.content):
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logger.debug(f"Tool response chunk {i}: {content.text}")
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response += content.text
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logger.info(f"Tool '{function_name}' completed successfully")
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logger.info(f"Final response: {response}")
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# Send result back through callback
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await result_callback(response)
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except Exception as e:
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error_msg = f"Error calling mcp.run tool {function_name}: {str(e)}"
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logger.error(error_msg)
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logger.exception("Full exception details:")
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await result_callback(error_msg)
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def register_mcp_tools(llm) -> ToolsSchema:
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"""Register all available mcp.run tools with the LLM service.
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Args:
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llm: The Pipecat LLM service to register tools with
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Returns:
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A ToolsSchema containing all registered tools
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"""
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logger.debug("Starting registration of mcp.run tools")
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tool_schemas: List[FunctionSchema] = []
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# Get all available tools from mcp.run
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available_tools = llm.mcp_client.tools
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logger.debug(f"Found {len(available_tools)} available tools")
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for tool_name, tool in available_tools.items():
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logger.debug(f"Processing tool: {tool_name}")
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logger.debug(f"Tool description: {tool.description}")
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try:
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# Convert the schema
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function_schema = convert_mcp_schema_to_pipecat(tool_name, {
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"description": tool.description,
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"input_schema": tool.input_schema
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})
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# Register the wrapped function
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logger.debug(f"Registering function handler for '{tool_name}'")
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llm.register_function(tool_name, mcp_tool_wrapper)
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# Add to our list of schemas
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tool_schemas.append(function_schema)
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logger.debug(f"Successfully registered tool '{tool_name}'")
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except Exception as e:
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logger.error(f"Failed to register tool '{tool_name}': {str(e)}")
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logger.exception("Full exception details:")
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continue
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logger.info(f"Completed registration of {len(tool_schemas)} tools")
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tools_schema = ToolsSchema(standard_tools=tool_schemas)
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return tools_schema
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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@@ -158,7 +41,7 @@ async def main():
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transport = DailyTransport(
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room_url,
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token,
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"bot with fetch mcp",
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"Bot with MCP tools",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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@@ -176,28 +59,18 @@ async def main():
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# llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
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# llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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# Initialize the mcp.run client
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logger.info("Initializing mcp.run client")
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mcp_client = Client()
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llm.mcp_client = mcp_client
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tools = register_mcp_tools(llm)
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mcp_run = MCPRun(llm)
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tools = mcp_run.register_mcp_tools(llm)
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system = """
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Always start with 'Aloha'. You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities
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You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities
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in a succinct way. You have access to various tools provided by mcp.run that you can use to help users.
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Your output will be converted to audio so don't include special characters in your answers.
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Respond to what the user said in a creative and helpful way. Don't overexplain what you are doing as it takes up too much time.
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Respond to what the user said in a creative and helpful way. Don't overexplain what you are doing.
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Just respond with short sentences when you are carrying out tool calls.
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Note: When the user asks you to to put something in Notion, use the parent page "Integration Page"
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which has the id: "15e9debf-7128-80f2-88d6-c5cabcba3ef8"
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"""
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messages = [{"role": "system",
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"content": system
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}
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]
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messages = [{"role": "system","content": system}]
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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141
src/pipecat/services/mcp_run/mcp_run.py
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141
src/pipecat/services/mcp_run/mcp_run.py
Normal file
@@ -0,0 +1,141 @@
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import os
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import json
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from typing import Any, Dict, List, Mapping, Optional, Union
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from loguru import logger
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from pipecat.services.llm_service import LLMService
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from mcp_run import Client
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try:
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from anthropic import NOT_GIVEN, AsyncAnthropic, NotGiven
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except ModuleNotFoundError as e:
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logger.error(f"Exception: {e}")
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logger.error(
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"In order to use mcp.run, you need to `pip install pipecat-ai[mcp_run]`. "
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+ "Also, set `MCP_RUN_SESSION_ID` environment variable."
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)
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raise Exception(f"Missing module: {e}")
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class MCPRun(Client):
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def __init__(
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self,
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llm: LLMService,
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mcp_run_session_id: Optional[str] = None,
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**kwargs,
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):
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super().__init__(**kwargs)
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self._client = Client()
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self._mcp_run_session_id = mcp_run_session_id or os.getenv("MCP_RUN_SESSION_ID")
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def convert_mcp_schema_to_pipecat(self, tool_name: str, tool_schema: dict[str, any]) -> FunctionSchema:
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"""Convert an mcp.run tool schema to Pipecat's FunctionSchema format.
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Args:
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tool_name: The name of the tool
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tool_schema: The mcp.run tool schema
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Returns:
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A FunctionSchema instance
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"""
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logger.debug(f"Converting schema for tool '{tool_name}'")
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logger.debug(f"Original schema: {json.dumps(tool_schema, indent=2)}")
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# Extract properties and required fields from the mcp.run schema
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properties = tool_schema["input_schema"].get("properties", {})
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required = tool_schema["input_schema"].get("required", [])
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schema = FunctionSchema(
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name=tool_name,
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description=tool_schema["description"],
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properties=properties,
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required=required
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)
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logger.debug(f"Converted schema: {json.dumps(schema.to_default_dict(), indent=2)}")
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return schema
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def register_mcp_tools(self, llm) -> ToolsSchema:
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"""Register all available mcp.run tools with the LLM service.
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Args:
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llm: The Pipecat LLM service to register tools with
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Returns:
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A ToolsSchema containing all registered tools
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"""
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async def mcp_tool_wrapper(function_name: str, tool_call_id: str, arguments: dict[str, any],
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llm: any, context: any, result_callback: any) -> None:
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"""Wrapper for mcp.run tool calls to match Pipecat's function call interface.
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"""
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logger.debug(f"Executing tool '{function_name}' with call ID: {tool_call_id}")
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logger.debug(f"Tool arguments: {json.dumps(arguments, indent=2)}")
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try:
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# Call the mcp.run tool
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logger.debug(f"Calling mcp.run tool '{function_name}'")
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results = self._client.call_tool(function_name, params=arguments)
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# Combine all content into a single response
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response = ""
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for i, content in enumerate(results.content):
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logger.debug(f"Tool response chunk {i}: {content.text}")
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response += content.text
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logger.info(f"Tool '{function_name}' completed successfully")
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logger.info(f"Final response: {response}")
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# Send result back through callback
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await result_callback(response)
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except Exception as e:
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error_msg = f"Error calling mcp.run tool {function_name}: {str(e)}"
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logger.error(error_msg)
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logger.exception("Full exception details:")
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await result_callback(error_msg)
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logger.debug("Starting registration of mcp.run tools")
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tool_schemas: List[FunctionSchema] = []
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# Get all available tools from mcp.run
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available_tools = self._client.tools
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logger.debug(f"Found {len(available_tools)} available tools")
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for tool_name, tool in available_tools.items():
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logger.debug(f"Processing tool: {tool_name}")
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logger.debug(f"Tool description: {tool.description}")
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try:
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# Convert the schema
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function_schema = self.convert_mcp_schema_to_pipecat(tool_name, {
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"description": tool.description,
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"input_schema": tool.input_schema
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})
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# Register the wrapped function
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logger.debug(f"Registering function handler for '{tool_name}'")
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llm.register_function(tool_name, mcp_tool_wrapper)
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# Add to our list of schemas
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tool_schemas.append(function_schema)
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logger.debug(f"Successfully registered tool '{tool_name}'")
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except Exception as e:
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logger.error(f"Failed to register tool '{tool_name}': {str(e)}")
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logger.exception("Full exception details:")
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continue
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logger.info(f"Completed registration of {len(tool_schemas)} tools")
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tools_schema = ToolsSchema(standard_tools=tool_schemas)
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return tools_schema
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