{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-mcp-evals","slug":"npm-mcp-evals","name":"mcp-evals","type":"mcp","url":"https://www.npmjs.com/package/mcp-evals","page_url":"https://unfragile.ai/npm-mcp-evals","categories":["mcp-servers"],"tags":["mcp","evaluation","github-actions","llm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-mcp-evals__cap_0","uri":"capability://automation.workflow.mcp.server.tool.call.evaluation.via.llm.scoring","name":"mcp server tool call evaluation via llm scoring","description":"Evaluates 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.","intents":["Automatically verify that my MCP server's tool implementations produce correct outputs without manual testing","Catch regressions in tool behavior when I refactor MCP server code","Generate quantitative quality metrics for tool call accuracy to track over time","Validate that tool calls match expected schemas and handle edge cases properly"],"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"],"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"],"requires":["GitHub Actions workflow environment (GitHub repository with Actions enabled)","MCP server implementation with callable tools/functions","API key for LLM provider (Anthropic Claude, OpenAI, or compatible API)","Node.js 16+ runtime","Test cases or expected outputs defined for tool calls being evaluated"],"input_types":["MCP tool call traces (function name, parameters, return values)","Expected outputs or success criteria (text descriptions or structured data)","Tool definitions/schemas (JSON or TypeScript interfaces)"],"output_types":["Numeric scores (0-1 range per tool call)","Evaluation reasoning (text explanation from LLM)","Aggregated metrics (pass/fail counts, average scores)","GitHub check run results or workflow artifacts"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcp-evals__cap_1","uri":"capability://automation.workflow.github.actions.workflow.integration.for.automated.tool.evaluation","name":"github actions workflow integration for automated tool evaluation","description":"Provides 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.","intents":["Set up continuous evaluation of my MCP server tools without writing custom CI/CD logic","Block PRs from merging if tool evaluation scores fall below a threshold","Track tool quality metrics over time across commits and branches","Get instant feedback on tool changes in pull request reviews"],"best_for":["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"],"limitations":["Tightly coupled to GitHub Actions — not portable to GitLab CI, CircleCI, or other CI/CD platforms","Workflow execution time depends on number of tool calls and LLM latency, potentially slowing down CI pipelines by 30-60 seconds per run","GitHub Actions secrets management required for API keys — adds operational overhead for key rotation","Limited to GitHub's runner environment constraints (timeout limits, resource quotas)","No built-in support for parallel evaluation across multiple runners — sequential evaluation only"],"requires":["GitHub repository with Actions enabled","GitHub Actions workflow file (.github/workflows/*.yml) to define trigger conditions","mcp-evals package installed as a GitHub Action or Node.js dependency","LLM API credentials stored as GitHub secrets","MCP server accessible or deployable within GitHub Actions environment"],"input_types":["GitHub Actions workflow configuration (YAML)","MCP server code or Docker image","Test case definitions (JSON or YAML)"],"output_types":["GitHub check runs with pass/fail status","PR comments with evaluation summaries","Workflow artifacts containing detailed evaluation logs","Status badges for README"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcp-evals__cap_2","uri":"capability://safety.moderation.llm.based.tool.call.correctness.scoring.with.structured.rubrics","name":"llm-based tool call correctness scoring with structured rubrics","description":"Implements 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.","intents":["Get quantitative scores for tool call quality without writing manual assertions","Define custom evaluation criteria specific to my tool's domain or business logic","Compare tool performance across versions or implementations using consistent metrics","Identify which tool calls are failing or underperforming in automated evaluations"],"best_for":["Teams building complex tools where correctness is hard to define with simple assertions","Projects requiring audit trails of tool evaluation decisions","Organizations wanting to measure tool quality improvements over time"],"limitations":["LLM scoring is subjective and non-deterministic — identical tool calls may score differently across runs or LLM versions","Requires careful prompt engineering to define rubrics — poorly written rubrics lead to inconsistent or meaningless scores","Cannot evaluate tools with non-deterministic outputs (e.g., random sampling, timestamp-dependent behavior) reliably","Scoring latency scales with number of tool calls — evaluating 100+ tool calls per run becomes expensive and slow","No support for evaluating tools that require external state or side effects (database writes, API calls to third-party services)"],"requires":["LLM API access (Anthropic Claude, OpenAI, or compatible)","Tool call traces with inputs and outputs","Evaluation rubric definition (as text prompt or structured template)","Expected outcomes or ground truth for comparison"],"input_types":["Tool call traces (JSON with function name, parameters, return value)","Expected outputs (text or structured data)","Evaluation rubric (text prompt template)"],"output_types":["Numeric scores (0-1 per tool call)","Evaluation reasoning (text from LLM)","Aggregated statistics (mean, median, distribution of scores)","Pass/fail classifications based on score thresholds"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcp-evals__cap_3","uri":"capability://automation.workflow.mcp.server.test.case.execution.and.result.collection","name":"mcp server test case execution and result collection","description":"Orchestrates 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.","intents":["Automatically execute a suite of tool calls against my MCP server without manual invocation","Capture tool outputs in a structured format for evaluation and comparison","Test multiple tool calls in sequence and collect all results for batch evaluation","Validate that my MCP server handles edge cases and error conditions correctly"],"best_for":["MCP server developers who want to test tool implementations in CI/CD","Teams building test suites for MCP servers","Projects requiring reproducible tool call execution for debugging"],"limitations":["Requires MCP server to be runnable in GitHub Actions environment — may fail for servers with external dependencies or special hardware","No built-in support for testing tools with side effects (file writes, database changes) — requires mocking or sandboxing","Test case execution is sequential — no parallelization, so large test suites become slow","Limited error handling for server crashes or timeouts — may require manual debugging","No support for testing tools that require authentication or external API keys within the test environment"],"requires":["MCP server implementation (Node.js, Python, or other supported runtime)","Test case definitions with tool names and parameters","MCP server accessible or deployable in CI/CD environment","Node.js 16+ for running the test orchestrator"],"input_types":["MCP server code or executable","Test case definitions (JSON with tool names, parameters, expected outputs)","Server configuration (environment variables, startup arguments)"],"output_types":["Tool call results (return values, error messages)","Execution logs (server output, timing information)","Structured result format (JSON with tool name, parameters, output, execution time)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcp-evals__cap_4","uri":"capability://automation.workflow.evaluation.result.reporting.and.github.integration","name":"evaluation result reporting and github integration","description":"Generates 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.","intents":["See evaluation results directly in my GitHub PR without leaving the platform","Block PRs from merging if tool evaluation scores are too low","Share evaluation metrics with team members via PR comments","Track evaluation history across commits using workflow artifacts"],"best_for":["GitHub-based teams wanting integrated quality feedback in their workflow","Projects with strict quality gates that require automated enforcement","Teams needing to communicate tool quality metrics to non-technical stakeholders"],"limitations":["GitHub API rate limits may be hit if evaluating many PRs in quick succession","PR comments are not updated — each evaluation creates a new comment, leading to comment spam on frequently-updated PRs","Check runs are tied to specific commits — if a PR is force-pushed, old evaluation results are orphaned","No support for custom report formatting beyond markdown — cannot generate PDF or HTML reports","Requires GitHub token with write permissions to repository — adds security considerations for token management"],"requires":["GitHub repository with Actions enabled","GitHub token with write permissions (provided automatically in Actions environment)","Evaluation results in structured format (JSON)","mcp-evals package with reporting functionality"],"input_types":["Evaluation results (JSON with scores, reasoning, pass/fail status)","Configuration for reporting (thresholds, report format, channels)"],"output_types":["GitHub check runs (pass/fail status)","PR comments (markdown formatted)","Workflow artifacts (JSON, CSV, or text logs)","Status badges (SVG for README)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["GitHub Actions workflow environment (GitHub repository with Actions enabled)","MCP server implementation 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