{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-mcp-evals","slug":"mcp-evals","name":"mcp-evals","type":"mcp","url":"https://www.mcpevals.io/","page_url":"https://unfragile.ai/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-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 call results to an LLM (OpenAI, Anthropic, or other providers) with configurable scoring rubrics. The system captures tool invocations from MCP server execution, constructs evaluation prompts with context about the original request and actual output, and returns structured scores (typically 0-10 or pass/fail) based on LLM judgment of whether the tool was called appropriately and produced useful results.","intents":["Automatically validate that my MCP server's tools are being called correctly in CI/CD pipelines","Score the quality of tool outputs without manual review","Detect regressions when tool behavior changes across versions","Generate quantitative metrics on tool call accuracy for monitoring"],"best_for":["Teams building and maintaining MCP servers who need automated quality gates","LLM application developers integrating MCP tools into agents","DevOps engineers setting up continuous evaluation in GitHub Actions workflows"],"limitations":["LLM-based scoring introduces non-deterministic results — same tool call may score differently across runs due to model variance","Requires external LLM API calls, adding latency (typically 1-5 seconds per evaluation) and cost per test run","Scoring quality depends entirely on rubric design — poorly written evaluation prompts produce unreliable scores","No built-in persistence of historical scores — requires external logging to track trends over time"],"requires":["GitHub Actions workflow environment","MCP server implementation with tool definitions","API key for at least one LLM provider (OpenAI, Anthropic, etc.)","Node.js 16+ or Python 3.8+ depending on implementation"],"input_types":["MCP tool call logs (JSON format with tool name, arguments, results)","Evaluation rubric (natural language or structured prompt template)","Original user request or context for the tool call"],"output_types":["Numeric scores (0-10 or 0-100 scale)","Pass/fail verdicts","Structured evaluation reports (JSON)","GitHub Actions check results with pass/fail status"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-evals__cap_1","uri":"capability://automation.workflow.github.actions.workflow.integration.for.automated.test.evaluation","name":"github actions workflow integration for automated test evaluation","description":"Provides a GitHub Action that runs as a workflow step, automatically triggering MCP server tool evaluations on pull requests, commits, or scheduled intervals. The action orchestrates test execution, captures tool call telemetry, invokes the LLM evaluation engine, and reports results back to GitHub as check runs, PR comments, or workflow artifacts, enabling developers to see evaluation scores without leaving their GitHub interface.","intents":["Run tool evaluations automatically on every PR to catch regressions before merge","Display evaluation scores as GitHub check results so developers see pass/fail status","Generate evaluation reports as workflow artifacts for historical tracking","Block merges if evaluation scores fall below a configured threshold"],"best_for":["GitHub-native teams with existing CI/CD workflows","MCP server maintainers who want zero-friction evaluation setup","Teams practicing continuous integration with automated quality gates"],"limitations":["GitHub Actions-only — no native support for GitLab CI, CircleCI, or other platforms","Workflow execution time depends on number of tool calls and LLM latency — can add 2-10 minutes to CI runs","GitHub API rate limits may throttle large-scale evaluations (e.g., 100+ tool calls per run)","Requires GitHub repository with Actions enabled — no support for self-hosted or on-premise scenarios without additional setup"],"requires":["GitHub repository with Actions enabled","GitHub Actions workflow file (.github/workflows/*.yml)","Valid LLM API credentials stored as GitHub Secrets","MCP server accessible or testable within GitHub Actions environment"],"input_types":["GitHub Actions event triggers (push, pull_request, schedule)","Workflow configuration (YAML)","Test definitions or tool call scenarios"],"output_types":["GitHub check runs (pass/fail status)","PR comments with evaluation summary","Workflow artifacts (JSON evaluation reports)","GitHub Actions logs with detailed scoring breakdown"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-evals__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.evaluation.with.configurable.scoring.rubrics","name":"multi-provider llm evaluation with configurable scoring rubrics","description":"Abstracts LLM provider selection (OpenAI, Anthropic, local models, etc.) behind a unified evaluation interface, allowing users to define custom scoring rubrics as natural language prompts or structured templates. The system routes evaluation requests to the configured provider, injects the rubric into the evaluation prompt, and normalizes responses into consistent score formats regardless of which LLM backend is used.","intents":["Use my preferred LLM provider (OpenAI, Claude, open-source) for tool evaluation without code changes","Define custom evaluation criteria specific to my tool's domain or use case","Switch LLM providers without rewriting evaluation logic","Optimize cost by choosing cheaper or faster models for evaluation"],"best_for":["Teams with existing LLM provider relationships or cost constraints","Organizations with specific compliance requirements (e.g., on-premise models only)","Developers who want to experiment with different LLM backends for evaluation quality"],"limitations":["Rubric quality is user-dependent — poorly written evaluation prompts produce unreliable scores regardless of LLM provider","Different LLM providers have different output formats and reasoning styles, potentially causing score variance across providers","No automatic rubric optimization — users must manually iterate on prompts to improve evaluation quality","Provider-specific features (e.g., structured outputs, vision capabilities) may not be uniformly supported across all backends"],"requires":["API credentials for at least one supported LLM provider","Evaluation rubric definition (natural language or prompt template)","Configuration file or environment variables specifying provider and model selection"],"input_types":["Tool call context (tool name, arguments, results, original request)","Evaluation rubric (text prompt or structured template)","Provider configuration (API key, model name, temperature, etc.)"],"output_types":["Numeric scores (normalized across providers)","Structured evaluation results (JSON with score, reasoning, pass/fail)","Provider-agnostic evaluation reports"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-evals__cap_3","uri":"capability://data.processing.analysis.tool.call.telemetry.capture.and.structured.logging","name":"tool call telemetry capture and structured logging","description":"Intercepts and logs MCP tool invocations with full context: tool name, input arguments, output results, execution time, and error states. Data is captured in structured JSON format with timestamps and request IDs, enabling downstream evaluation systems to access complete call history and correlate evaluations with specific invocations across distributed systems.","intents":["Capture detailed logs of which tools were called and what they returned during test execution","Correlate tool calls with evaluation scores for debugging and analysis","Export tool call telemetry for external analysis or archival","Detect patterns in tool usage (e.g., which tools are called most frequently)"],"best_for":["Teams running MCP servers in production or testing environments who need observability","Developers debugging tool call failures or unexpected behavior","Data analysts studying tool usage patterns and effectiveness"],"limitations":["Logging overhead adds latency to tool execution — structured JSON serialization can add 10-50ms per call","No built-in log retention or cleanup — logs accumulate indefinitely without external storage management","Sensitive data in tool arguments/results is logged as-is — requires external redaction or PII filtering","Log volume can become large at scale — 1000+ tool calls per run generates multi-MB log files"],"requires":["MCP server with instrumentation hooks or middleware support","Logging destination (file system, cloud storage, or log aggregation service)","Structured logging library compatible with MCP protocol"],"input_types":["MCP tool invocation events (tool name, arguments, results)","Execution context (request ID, timestamp, caller information)"],"output_types":["Structured JSON logs with tool call details","Log files or streaming log output","Telemetry data for downstream evaluation systems"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-evals__cap_4","uri":"capability://data.processing.analysis.regression.detection.via.score.trend.analysis","name":"regression detection via score trend analysis","description":"Tracks evaluation scores across multiple runs (commits, PRs, scheduled evaluations) and detects statistically significant regressions or improvements in tool call quality. The system compares current scores against historical baselines, flags scores that drop below thresholds, and generates trend reports showing score evolution over time.","intents":["Automatically detect when tool evaluation scores drop compared to previous runs","Block PRs if evaluation scores regress below acceptable thresholds","Visualize score trends over time to identify patterns or systemic issues","Alert teams when tool quality degrades unexpectedly"],"best_for":["Teams with continuous evaluation pipelines who want automated regression detection","MCP server maintainers tracking quality metrics across releases","Organizations with SLAs on tool quality and needing automated compliance monitoring"],"limitations":["Requires historical score data — first run has no baseline for comparison","Statistical significance thresholds must be tuned per use case — no universal defaults","Score variance from LLM non-determinism can trigger false-positive regressions","No built-in persistence of historical scores — requires external database or artifact storage"],"requires":["Multiple evaluation runs with captured scores (at least 2-3 baseline runs)","Historical score data stored in accessible format (JSON files, database, etc.)","Threshold configuration for regression detection (e.g., 10% score drop triggers alert)"],"input_types":["Current evaluation scores","Historical score data from previous runs","Regression threshold configuration"],"output_types":["Regression alerts (pass/fail on regression check)","Trend reports (JSON with score history and statistics)","GitHub check results indicating regression status"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-evals__cap_5","uri":"capability://automation.workflow.evaluation.result.reporting.and.github.integration","name":"evaluation result reporting and github integration","description":"Formats evaluation results into human-readable reports and integrates with GitHub's native reporting mechanisms: check runs (pass/fail status on commits), PR comments (inline feedback), and workflow artifacts (detailed JSON reports). The system normalizes evaluation data into GitHub-compatible formats and automatically posts results without requiring manual GitHub API calls.","intents":["See evaluation results directly in GitHub PR checks without visiting external dashboards","Get inline PR comments with evaluation feedback and scores","Download detailed evaluation reports as workflow artifacts for archival","Use GitHub branch protection rules to require passing evaluations before merge"],"best_for":["GitHub-native teams who want evaluation results in their existing workflow","Teams using branch protection rules and needing evaluation status as a merge requirement","Developers who prefer not to context-switch to external evaluation dashboards"],"limitations":["GitHub API rate limits restrict number of check runs and PR comments per hour","Check run descriptions have character limits (65,535 chars) — very detailed reports may be truncated","PR comments are posted sequentially — large numbers of comments can clutter PR discussion","No native support for rich formatting — reports are plain text or basic markdown"],"requires":["GitHub Actions workflow with appropriate permissions (checks:write, pull-requests:write)","GitHub token with sufficient scopes for check run and PR comment creation","Evaluation results in structured format (JSON)"],"input_types":["Evaluation results (scores, pass/fail, reasoning)","GitHub context (commit SHA, PR number, branch)"],"output_types":["GitHub check runs (visible in commit status)","PR comments (visible in PR discussion)","Workflow artifacts (downloadable JSON reports)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-evals__cap_6","uri":"capability://automation.workflow.configurable.evaluation.thresholds.and.pass.fail.criteria","name":"configurable evaluation thresholds and pass/fail criteria","description":"Allows users to define scoring thresholds, pass/fail criteria, and conditional logic for determining whether evaluations succeed or fail. Users can set minimum score requirements (e.g., 'score >= 7 to pass'), define multiple evaluation criteria with different thresholds, and configure weighted scoring if multiple tools are evaluated together.","intents":["Define what score constitutes a passing evaluation for my specific use case","Set different thresholds for different tool categories or criticality levels","Fail CI/CD pipelines if evaluation scores don't meet minimum standards","Adjust thresholds over time as tool quality improves"],"best_for":["Teams with domain-specific quality standards that differ from defaults","Organizations with tiered tool criticality (some tools require higher scores than others)","Teams iterating on tool quality and wanting to gradually raise standards"],"limitations":["Threshold tuning is manual and use-case-specific — no automatic optimization","Overly strict thresholds can cause false-positive failures due to LLM variance","No built-in guidance on reasonable threshold values — users must experiment","Threshold changes require code/config updates and redeployment"],"requires":["Configuration file or environment variables defining thresholds","Evaluation results in numeric format (scores, not just pass/fail)"],"input_types":["Evaluation scores (numeric)","Threshold configuration (minimum score, pass/fail logic)"],"output_types":["Pass/fail verdict based on threshold comparison","Detailed pass/fail reasoning (which criteria passed/failed)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-evals__cap_7","uri":"capability://data.processing.analysis.batch.evaluation.of.multiple.tool.calls.with.aggregated.scoring","name":"batch evaluation of multiple tool calls with aggregated scoring","description":"Processes multiple tool calls in a single evaluation run, scoring each call individually and then aggregating results into summary metrics (average score, pass rate, failure breakdown). The system batches LLM API calls for efficiency, correlates individual scores with specific tools, and generates aggregate reports showing overall tool quality across the batch.","intents":["Evaluate all tool calls from a test run in one batch operation","Get aggregate metrics like 'X% of tool calls passed' without evaluating each call separately","Identify which specific tools are failing most frequently","Reduce LLM API costs by batching evaluations"],"best_for":["Teams running test suites with dozens or hundreds of tool calls per run","Cost-conscious teams wanting to minimize LLM API calls","Teams needing aggregate quality metrics across tool portfolios"],"limitations":["Batching adds latency — all calls must complete before aggregation begins","Aggregate metrics can mask individual tool failures — a 90% pass rate hides which 10% failed","LLM API rate limits may throttle large batches — 100+ calls per batch may hit provider limits","Aggregation logic is fixed — no support for custom aggregation functions or weighted averages"],"requires":["Multiple tool calls to evaluate (minimum 2-3 for meaningful aggregation)","Batch evaluation configuration (batch size, aggregation method)"],"input_types":["Array of tool calls (tool name, arguments, results)","Evaluation rubric applied to each call"],"output_types":["Individual scores for each tool call","Aggregate metrics (average score, pass rate, failure breakdown)","Per-tool summary (e.g., 'Tool A: 8/10 calls passed')"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["GitHub Actions workflow environment","MCP server implementation with tool definitions","API key for at least one LLM provider (OpenAI, Anthropic, etc.)","Node.js 16+ or Python 3.8+ depending on implementation","GitHub repository with Actions enabled","GitHub Actions workflow file (.github/workflows/*.yml)","Valid LLM API credentials stored as GitHub Secrets","MCP server accessible or testable within GitHub Actions environment","API credentials for at least one supported LLM provider","Evaluation rubric definition (natural language or prompt template)"],"failure_modes":["LLM-based scoring introduces non-deterministic results — same tool call may score differently across runs due to model variance","Requires external LLM API calls, adding latency (typically 1-5 seconds per evaluation) and cost per test run","Scoring quality depends entirely on rubric design — poorly written evaluation prompts produce unreliable scores","No built-in persistence of historical scores — requires external logging to track trends over time","GitHub Actions-only — no native support for GitLab CI, CircleCI, or other platforms","Workflow execution time depends on number of tool calls and LLM latency — can add 2-10 minutes to CI runs","GitHub API rate limits may throttle large-scale evaluations (e.g., 100+ tool calls per run)","Requires GitHub repository with Actions enabled — no support for self-hosted or on-premise scenarios without additional setup","Rubric quality is user-dependent — poorly written evaluation prompts produce unreliable scores regardless of LLM provider","Different LLM providers have different output formats and reasoning styles, potentially causing score variance across providers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6975520742912585,"quality":0.26,"ecosystem":0.52,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.652Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":153720,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-evals","compare_url":"https://unfragile.ai/compare?artifact=mcp-evals"}},"signature":"4kMAUJ8/n/XyKm++JqML8Nj9BYoFgto72e8eruR5VkoJkbDUp0vFT6iJzzs2dW9yV3KVeSvURecfz2ojnJo7Dg==","signedAt":"2026-06-20T04:52:50.860Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-evals","artifact":"https://unfragile.ai/mcp-evals","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-evals","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}