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Run LLM evaluations, experiments, and custom evaluators through a standardized MCP interface.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Patronus MCP Server" yet — see the docs or source repo.
Use Patronus MCP Server to evaluate two customer support prompt versions, compare accuracy, hallucination rate, and tone consistency, and return a score table with conclusions.
A comparative evaluation with metric scores, strengths and weaknesses, and a recommended version.
Use Patronus MCP Server to run batch experiments on this set of RAG QA samples, test how different retrieval parameters affect answer quality, and summarize the results.
An experiment report with configurations, performance comparisons, and recommended parameter settings.
Use Patronus MCP Server to create a custom evaluator that checks whether recruiting assistant responses contain discriminatory language, and explain the evaluation rules with sample results.
A custom evaluator definition, decision criteria, and evaluation results for sample inputs.
Run prompt and RAG evaluations through MCP clients with hosted backend execution.
Connect to the mcp API via MCP to extend AI tool capabilities.
Build, debug, and manage software tasks with natural language across LLMs.
Evaluate AI response quality and behavior patterns during development in real time.
Route prompts across LLM providers with policy-based orchestration and verification.
Route LLM requests across providers and orchestrate MCP tools with local privacy.