Patronus MCP Server

Patronus AI is a research lab developing training data and simulated environments for frontier AI. Its work spans reinforcement learning environments and digital world models for agents operating in software. This integration reflects the company’s earlier work in LLM evaluation. The Patronus evaluation API it connects to has been retired, so this page is kept for reference.

What does “Patronus” MCP Server do?

The Patronus MCP (Model Context Protocol) Server is a standardized server implementation built for the Patronus SDK, designed to facilitate advanced LLM (Large Language Model) system optimizations, evaluations, and experiments. By connecting AI assistants to external data sources and services, Patronus MCP Server enables streamlined workflows for developers and researchers. It allows users to run single or batch evaluations, execute experiments with datasets, and initialize projects with specific API keys and settings. This extensible platform helps automate repetitive evaluation tasks, supports the integration of custom evaluators, and provides a robust interface for managing and analyzing LLM behavior, ultimately enhancing the AI development lifecycle.

List of Prompts

No prompt templates are explicitly listed in the repository or documentation.

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List of Resources

No explicit resources are detailed in the available documentation or repo files.

List of Tools

• initialize
  Initializes Patronus with API key, project, and application settings. Sets up the system for further evaluations and experiments.

• evaluate
  Runs a single evaluation using a configurable evaluator on given task inputs, outputs, and context.

• batch_evaluate
  Executes batch evaluations with multiple evaluators over provided tasks, producing collective results.

• run_experiment
  Runs experiments using datasets and specified evaluators, useful for benchmarking and comparison.

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Use Cases of this MCP Server

• LLM Evaluation Automation
  Automate the evaluation of large language models by batching tasks and applying multiple evaluators, reducing manual effort in quality assurance and benchmarking.

• Custom Experimentation
  Run tailored experiments with custom datasets and evaluators to benchmark new LLM architectures and compare performance across different criteria.

• Project Initialization for Teams
  Quickly set up and configure evaluation environments for multiple projects using API keys and project settings, streamlining onboarding and collaboration.

• Interactive Live Testing
  Use the provided scripts to interactively test evaluation endpoints, making it easier for developers to debug and validate their evaluation workflows.

How to set it up

The Patronus evaluation API has been retired, and the public Patronus MCP Server repository is no longer available. The steps below are kept for reference only.

Windsurf

1. Ensure you have Python and all dependencies installed.
2. Locate your Windsurf configuration file (e.g., .windsurf or windsurf.json).
3. Add the Patronus MCP Server with the following JSON snippet:

   {
     "mcpServers": [
       {
         "command": "python",
         "args": ["src/patronus_mcp/server.py"],
         "env": {
           "PATRONUS_API_KEY": "your_api_key_here"
         }
       }
     ]
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the server is running and accessible.

Claude

1. Install Python and dependencies.
2. Edit Claude’s configuration file.
3. Add Patronus MCP Server with:

   {
     "mcpServers": [
       {
         "command": "python",
         "args": ["src/patronus_mcp/server.py"],
         "env": {
           "PATRONUS_API_KEY": "your_api_key_here"
         }
       }
     ]
   }
   

4. Save changes and restart Claude.
5. Check connection to ensure proper setup.

Cursor

1. Set up Python environment and install requirements.
2. Open Cursor’s configuration file.
3. Add the Patronus MCP Server configuration:

   {
     "mcpServers": [
       {
         "command": "python",
         "args": ["src/patronus_mcp/server.py"],
         "env": {
           "PATRONUS_API_KEY": "your_api_key_here"
         }
       }
     ]
   }
   

4. Save the file and restart Cursor.
5. Confirm that the server is available to Cursor.

Cline

1. Confirm you have Python and required packages installed.
2. Access the Cline configuration file.
3. Insert the Patronus MCP Server entry:

   {
     "mcpServers": [
       {
         "command": "python",
         "args": ["src/patronus_mcp/server.py"],
         "env": {
           "PATRONUS_API_KEY": "your_api_key_here"
         }
       }
     ]
   }
   

4. Save and restart Cline.
5. Test the integration for successful setup.

Securing API Keys:
Place sensitive credentials like PATRONUS_API_KEY in the env object of your configuration. Example:

{
  "command": "python",
  "args": ["src/patronus_mcp/server.py"],
  "env": {
    "PATRONUS_API_KEY": "your_api_key_here"
  },
  "inputs": {}
}

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:

{
  "patronus-mcp": {
    "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 “patronus-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Roots Support: Not mentioned in the documentation or code.

Based on the information above, Patronus MCP Server provides a solid foundation and essential features for LLM evaluation and experimentation, but lacks documentation or implementation details for prompt templates, resources, and advanced MCP features like Roots and Sampling.

Our opinion

The Patronus MCP Server offers robust evaluation tools and clear setup instructions, but is missing standardized prompts, resource definitions, and some advanced MCP features. It is best suited for technical users focused on LLM evaluation and experimentation. Score: 6/10

MCP Score