Input validation error: '1.57' is not of type 'number' from langchain_mcp_adapter

I am getting the following error : ❌ An error occurred: Input validation error: '1.57' is not of type 'number' .

From what I understand, this happens before the MCP server is called: LangChain’s MCP adapter is validating the tool call arguments against the JSON Schema and the model is emitting "1.57" as a string instead of 1.57 as a number, so the validation fails.

Is that understanding correct? And what’s the recommended way to deal with this when using create_agent(model, tools) and MCP tools

Code :

import os import asyncio from dotenv import load_dotenv from langchain.agents import create_agent from langchain.chat_models import init_chat_model from langchain_mcp_adapters.client import MultiServerMCPClient import logging logging.basicConfig(level=logging.DEBUG) logging.getLogger("langchain_mcp_adapters").setLevel(logging.DEBUG) logging.getLogger("mcp").setLevel(logging.DEBUG) load_dotenv() async def main(): try: # 1.Get tools from MCP server mcp_server_url = os.getenv("MCP_SERVER_URL", "http://localhost:3001/mcp") client = MultiServerMCPClient({ "nav2_mcp_server": { "transport": "streamable_http", "url": mcp_server_url, } }) print("🔧 Fetching tools from Nav2 MCP server...") tools = await client.get_tools() print(f"✅ Retrieved {len(tools)} tools from physical-agentic-robot-mcp server:") for tool in tools: # print(f" • {tool.name}: {tool.description}") print(f" • {tool.name}: {tool.args_schema}") # 2.Create LLM model model = init_chat_model(model = "llama3.2:3b", model_provider="ollama") # 3. Create agent with tools from MCP server agent = create_agent(model, tools, system_prompt="You are the brains of the robot and need to call the correct tools with correct tool json schema arguments.") # 4. Use the agent to get documentation # query = "Is there any green trash can in environment, can you move there?" query = "Navigate 1m forward, then turn 90 degrees to the right, and move another 1m forward." print(f"👤 User: {query}\n") response = await agent.ainvoke({"messages": [("human", query)]}) last_message = response["messages"][-1] print(f"🤖 Agent: {last_message.content}\n") except Exception as e: print(f"❌ An error occurred: {e}") finally: print("✅ MCP client connection closed") if __name__ == "__main__": asyncio.run(main())

MCP server tool example

@mcp.tool( name='navigate_to_pose', description="""Navigate the robot to a specific pose (position and orientation) in the map frame. Example usage: - navigate to position (2.0, 3.0) with orientation 1.57 - go to x=2 y=3 with yaw=90 degrees - move to coordinates (2, 3) facing north """, tags={'navigate', 'go to', 'move to', 'navigate to pose', 'position'}, annotations={ 'title': 'Navigate To Pose', 'readOnlyHint': False, 'openWorldHint': False }, ) async def navigate_to_pose( x: Annotated[float, 'X coordinate of target pose in map frame'], y: Annotated[float, 'Y coordinate of target pose in map frame'], yaw: Annotated[ float, 'Orientation in radians (0=east, π/2=north, π=west, 3π/2=south)' ] = 0.0, ctx: Annotated[ Optional[Context], 'MCP context used for logging and progress msgs' ] = None, ) -> str: