Issue with MCP Server and Pydantic Model object as tool parameter in Cursor

Bug Reports
1

Problem Description

Whenever I create an MCP server using fastmcp, and in my tools when I add a Pydantic model as an argument, Cursor throws an error when it tries to call the tool:

Logs in Cursor:

{
    "clientMessage": "Invalid type for parameter 'agent' in tool get_agent_model",
    "modelMessage": "Parameter 'agent' must be of type undefined, got object",
    "actualError": "Type mismatch for get_agent_model.agent: expected undefined, got object"
}

It seems to try to pass the correct object. I checked the tool call in Cursor logs:

{
    "params": {
        "case": "mcpParams",
        "value": {
            "tools": [
                {
                    "name": "get_agent_model",
                    "description": "\n        An AI assistant that helps gather agent details and return the agent model\n        \n        # Rules\n        - Ask the user for the agent name\n        - Ask the user for the agent description\n        \n        Then return the agent model JSON\n\n        Args:\n            agent: The agent to get the model for\n        Returns:\n            agent: The agent to get the model for\n        ",
                    "parameters": "{\"agent\":{\"name\":\"RandomAgent\",\"description\":\"An experimental agent designed to explore random data patterns and generate unexpected insights.\"}}",
                    "serverName": "mcp_test"
                }
            ]
        }
    }
}

Example Code

Example using Pydantic Model as Input

from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field

mcp = FastMCP("test-mcp")

class Agent(BaseModel):
    """Agent class for managing agent definitions."""
    
    name: str = Field(..., description="Name of the agent")
    description: str = Field("", description="Description of the agent")

@mcp.tool()
def get_agent_model(agent: Agent):
    """
    An AI assistant that helps gather agent details and return the agent model
    
    # Rules
    - Ask the user for the agent name
    - Ask the user for the agent description
    
    Then return the agent model JSON

    Args:
        agent: The agent to get the model for
    Returns:
        agent: The agent to get the model for
    """
    return agent

def main():
    mcp.run(transport="stdio")

Example without Using Pydantic Model as Input

I tried using the tool without a Pydantic model as an argument, and it works fine.

@mcp.tool()
def get_agent_model(name: str, description: str):
    """
    An AI assistant that helps gather agent details and return the agent model
    
    # Rules
    - Ask the user for the agent name
    - Ask the user for the agent description
    
    Then return the agent model JSON

    Args:
        name: The name of the agent
        description: The description of the agent
    Returns:
        agent: The agent to get the model for
    """
    return Agent(name=name, description=description)

Question

This works fine in Claude Desktop. Is this a current limitation in Cursor?

2 Likes
2

I had something similar happen! For some reason this happens less now… So not sure what has changed now. But just to give another data point :slight_smile:

3

Thanks,

As a workaround I ended up using the lower level implementation of mcp server instead of FastMCP in python like in here: python-sdk/examples/servers/simple-tool/mcp_simple_tool/server.py at main · modelcontextprotocol/python-sdk · GitHub

And in the input schema I render the Pydantic models to json schema like:

 inputSchema={
                "type": "object",
                "properties": {
                    "agent_metadata": AgentMetadata.model_json_schema(),
                },
                "required": ["agent_metadata"],
            },

That seems to work better,

1 Like
4

+1 to this issue. Here’s a simple test case.

Cursor has no problems calling this add tool:

@mcp.tool()
async def add(a: int, b: int):
    return a + b

But Cursor is unable to compose the request for this add_pydantic tool:

class AddRequest(BaseModel):
    a: int
    b: int

@mcp.tool()
async def add_pydantic(request: AddRequest):
    return request.a + request.b

Claude Desktop and Windsurf do not have this problem. I’ve also made sure to use the Claude 3.7 Sonnet model (same as I used in Claude Desktop), just in case it was an issue at the LLM layer.

5

The same behavior happens with the typescript sdk using zod based definitions.
I’m trying to build a more complex mcp tool at the moment and this is a show stopper for me :frowning:

1 Like