mcp-evals
MCP ServerFreeGitHub Action for evaluating MCP server tool calls using LLM-based scoring
Capabilities5 decomposed
mcp server tool call evaluation via llm scoring
Medium confidenceEvaluates the correctness and quality of tool calls made by MCP servers by submitting them to an LLM for scoring against expected outcomes. Uses a prompt-based evaluation framework that sends tool call traces (input parameters, outputs, side effects) to Claude or other LLMs, which return structured scores (0-1 range) and reasoning. Integrates with GitHub Actions to run evaluations on every commit or pull request, storing results as workflow artifacts or check runs.
Specifically designed for MCP server validation using LLM-based scoring within GitHub Actions, providing automated quality gates for tool implementations without requiring manual test case writing. Uses MCP protocol semantics to extract and evaluate tool call traces directly from server responses.
More specialized for MCP servers than generic LLM evaluation frameworks, and integrates natively with GitHub Actions workflows rather than requiring separate test infrastructure or external platforms.
github actions workflow integration for automated tool evaluation
Medium confidenceProvides a reusable GitHub Action that can be invoked in CI/CD pipelines to run MCP tool evaluations on every push, pull request, or scheduled trigger. Handles workflow orchestration including: spinning up MCP server instances, executing test tool calls, collecting results, and reporting back to GitHub (check runs, status badges, PR comments). Manages authentication with LLM providers and stores evaluation results as workflow artifacts for historical tracking.
Native GitHub Actions integration that treats MCP server evaluation as a first-class CI/CD step, with built-in support for check runs, PR comments, and artifact storage rather than requiring custom glue code.
Simpler to set up than building custom CI/CD logic or using generic test runners, because it understands MCP protocol semantics and GitHub Actions conventions natively.
llm-based tool call correctness scoring with structured rubrics
Medium confidenceImplements a scoring engine that sends tool call traces to an LLM with a structured evaluation rubric, receiving back numeric scores (0-1) and reasoning. The rubric defines evaluation criteria (correctness, completeness, error handling, performance) and the LLM applies these criteria to assess whether a tool call produced the expected outcome. Supports custom rubrics via prompt templates, allowing teams to define domain-specific evaluation criteria. Returns both individual tool call scores and aggregated metrics across test suites.
Uses LLM-based rubric evaluation specifically for MCP tool calls, allowing semantic assessment of tool correctness rather than relying on brittle regex or assertion-based testing. Supports custom rubrics to encode domain-specific evaluation logic.
More flexible than assertion-based testing for complex tool outputs, and more interpretable than black-box ML-based evaluation because it provides LLM reasoning alongside scores.
mcp server test case execution and result collection
Medium confidenceOrchestrates the execution of test cases against an MCP server by: (1) starting the MCP server process, (2) invoking specified tool calls with test parameters, (3) capturing outputs and side effects, (4) collecting results into a structured format for evaluation. Handles MCP protocol communication (JSON-RPC over stdio or HTTP), manages server lifecycle (startup, shutdown, error handling), and normalizes tool call results into a consistent schema for downstream evaluation. Supports both local server instances and remote MCP servers.
Handles full MCP protocol lifecycle management (server startup, JSON-RPC communication, result collection) specifically for test execution, abstracting away MCP protocol details from evaluation logic.
More complete than manual tool invocation because it manages server lifecycle and normalizes results, and more MCP-aware than generic test runners that don't understand MCP semantics.
evaluation result reporting and github integration
Medium confidenceGenerates and publishes evaluation results back to GitHub using multiple reporting channels: check runs (pass/fail status on commits), PR comments (detailed evaluation summaries), workflow artifacts (raw evaluation logs), and status badges. Formats results for human readability (markdown tables, charts) and machine readability (JSON exports). Supports threshold-based pass/fail decisions to block PRs or trigger notifications. Integrates with GitHub's check runs API to provide inline feedback on specific commits.
Multi-channel reporting that leverages GitHub's native check runs and PR comment APIs to provide contextual feedback at the point of code review, rather than requiring developers to check a separate dashboard.
More integrated into GitHub's native workflow than external dashboards or email reports, reducing friction for developers to see and act on evaluation results.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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GitHub Action for evaluating MCP server tool calls using LLM-based scoring
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Best For
- ✓MCP server developers building and iterating on tool implementations
- ✓Teams maintaining multiple MCP servers who need continuous quality gates
- ✓Developers integrating MCP servers into production systems and requiring validation
- ✓GitHub-based teams using standard Actions workflows
- ✓MCP server projects with frequent tool updates requiring quality gates
- ✓Organizations wanting to enforce tool quality standards across multiple repositories
- ✓Teams building complex tools where correctness is hard to define with simple assertions
- ✓Projects requiring audit trails of tool evaluation decisions
Known Limitations
- ⚠LLM-based scoring is non-deterministic — same tool call may receive different scores across runs due to model variance
- ⚠Requires API calls to external LLM provider (Anthropic, OpenAI, etc.), adding latency (~2-5s per evaluation) and cost per evaluation run
- ⚠Evaluation quality depends entirely on prompt engineering — poorly written evaluation prompts will produce unreliable scores
- ⚠No built-in support for evaluating tool calls with side effects (file writes, API calls) — requires mocking or sandboxing
- ⚠Limited to GitHub Actions environment — cannot be easily run locally or in other CI/CD systems without adaptation
- ⚠Tightly coupled to GitHub Actions — not portable to GitLab CI, CircleCI, or other CI/CD platforms
Requirements
Input / Output
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GitHub Action for evaluating MCP server tool calls using LLM-based scoring
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