pydanticpydantic-aimcp-run-python MCP Server

What does “pydanticpydantic-aimcp-run-python” MCP Server do?

The pydanticpydantic-aimcp-run-python MCP Server is designed to serve as a bridge between AI assistants and Python code execution environments. By exposing a secure and controlled interface for running Python scripts, this MCP Server enables AI clients to interact programmatically with Python functions, automate computation workflows, and retrieve results as part of broader development pipelines. This capability is particularly valuable for tasks like dynamic code evaluation, rapid prototyping, or integrating Python-based analysis within LLM-driven automation. The server empowers developers to streamline coding, debugging, and data processing by connecting their AI tools with live Python execution—while maintaining clear security and operational boundaries.

List of Prompts

No prompt templates are mentioned in the repository files or documentation.

List of Resources

No specific resource primitives are mentioned in the available repository content.

List of Tools

• functions
  The functions namespace is present, but no explicit tools are defined within it according to the repo content.
• multi_tool_use.parallel
  Enables running multiple tools simultaneously in parallel, provided that the tools are from the functions namespace and are capable of being executed concurrently. Useful for distributing workloads or batch processing within the MCP context.

Use Cases of this MCP Server

• Dynamic Python Code Execution
  Allow LLMs or AI clients to execute arbitrary Python scripts in a controlled environment, supporting rapid prototyping and iterative development without manual intervention.
• Automated Data Analysis
  Integrate live Python processing (e.g., pandas, numpy) into AI workflows, enabling fast, in-the-loop data analysis and reporting driven by LLM-powered agents.
• Parallel Task Execution
  Utilize the multi_tool_use.parallel capability to execute multiple Python functions concurrently, optimizing workflows that benefit from parallelism.
• CI/CD Integration
  Embed Python code execution in automated testing, code validation, or deployment pipelines managed by AI assistants, improving reliability and developer productivity.
• Education and Experimentation
  Provide a safe sandbox for students or researchers to run and tweak Python code as part of interactive tutorials or scientific exploration using LLM guidance.

How to set it up

Windsurf

1. Ensure Node.js is installed and your Windsurf environment is up to date.
2. Open your Windsurf configuration file.
3. Add the pydanticpydantic-aimcp-run-python MCP Server under the mcpServers section:

   {
     "mcpServers": {
       "pydanticpydantic-aimcp-run-python": {
         "command": "npx",
         "args": [
           "@pydanticpydantic-aimcp-run-python@latest",
           "start"
         ]
       }
     }
   }
   

4. Save your configuration and restart Windsurf.
5. Verify the server is available within Windsurf.

Claude

1. Install Node.js and ensure Claude has MCP support.
2. Locate the Claude configuration file.
3. Insert the following MCP server configuration:

   {
     "mcpServers": {
       "pydanticpydantic-aimcp-run-python": {
         "command": "npx",
         "args": [
           "@pydanticpydantic-aimcp-run-python@latest",
           "start"
         ]
       }
     }
   }
   

4. Save and restart the Claude application.
5. Confirm the MCP server is recognized and functional.

Cursor

1. Install or update Node.js and Cursor.
2. Edit Cursor’s MCP server settings.
3. Add the MCP server configuration:

   {
     "mcpServers": {
       "pydanticpydantic-aimcp-run-python": {
         "command": "npx",
         "args": [
           "@pydanticpydantic-aimcp-run-python@latest",
           "start"
         ]
       }
     }
   }
   

4. Save your changes and restart Cursor.
5. Check that the MCP server is listed and active.

Cline

1. Make sure Node.js is installed and Cline is configured for MCP integration.
2. Open the relevant Cline configuration file.
3. Add the following MCP entry:

   {
     "mcpServers": {
       "pydanticpydantic-aimcp-run-python": {
         "command": "npx",
         "args": [
           "@pydanticpydantic-aimcp-run-python@latest",
           "start"
         ]
       }
     }
   }
   

4. Save and restart Cline.
5. Validate the MCP server’s connectivity.

Securing API Keys

For security, define your API keys and secrets in environment variables, not directly in configuration files. Reference them using the env field and pass them as needed in the inputs section. Example:

{
  "mcpServers": {
    "pydanticpydantic-aimcp-run-python": {
      "command": "npx",
      "args": [
        "@pydanticpydantic-aimcp-run-python@latest",
        "start"
      ],
      "env": {
        "PYTHON_API_KEY": "${PYTHON_API_KEY}"
      },
      "inputs": {
        "api_key": "${PYTHON_API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "pydanticpydantic-aimcp-run-python": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “pydanticpydantic-aimcp-run-python” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the available information, this MCP server provides basic Python execution and parallel tool orchestration, but lacks prompt templates, resource primitives, and explicit sampling or roots support. Its major strengths are straightforward integration and clear security recommendations. Improvements could be made by adding more tools, prompts, and documentation on advanced MCP features.

Our opinion

This MCP server is functionally useful for Python code execution and parallelism, but the lack of prompts, resources, and explicit advanced MCP features makes it more of a basic integration. The codebase is minimal, and documentation on nuanced capabilities is limited.

MCP Score

Overall, I would rate this MCP server a 4/10 for foundational utility but limited feature set and documentation.