What can mcp-evals do?

mcp server tool call evaluation via llm scoring, github actions workflow integration for automated test evaluation, multi-provider llm evaluation with configurable scoring rubrics, tool call telemetry capture and structured logging, regression detection via score trend analysis, evaluation result reporting and github integration, configurable evaluation thresholds and pass/fail criteria, batch evaluation of multiple tool calls with aggregated scoring

mcp-evals

MCP ServerFree

GitHub Action for evaluating MCP server tool calls using LLM-based scoring

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

mcp server tool call evaluation via llm scoring

Medium confidence

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.

Solves for

Automatically validate that my MCP server's tools are being called correctly in CI/CD pipelinesScore the quality of tool outputs without manual reviewDetect regressions when tool behavior changes across versionsGenerate 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

Requires

GitHub Actions workflow environment

MCP server implementation with tool definitions

API key for at least one LLM provider (OpenAI, Anthropic, etc.)

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

What makes it unique

Purpose-built for MCP server evaluation in GitHub Actions workflows, integrating directly with MCP protocol semantics (tool schemas, call arguments, results) rather than generic LLM evaluation — understands MCP-specific context like tool definitions and server capabilities to construct more relevant evaluation prompts

vs alternatives

More specialized than generic LLM evaluation frameworks (like Braintrust or Weights & Biases) because it natively understands MCP tool call structure and integrates directly into GitHub Actions, reducing setup friction for MCP-specific teams

github actions workflow integration for automated test evaluation

Medium confidence

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.

Solves for

Run tool evaluations automatically on every PR to catch regressions before mergeDisplay evaluation scores as GitHub check results so developers see pass/fail statusGenerate evaluation reports as workflow artifacts for historical trackingBlock 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

Requires

GitHub repository with Actions enabled

GitHub Actions workflow file (.github/workflows/*.yml)

Valid LLM API credentials stored as GitHub Secrets

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)

What makes it unique

Tight GitHub Actions integration with native check run reporting and PR comment support, allowing evaluation results to flow directly into GitHub's native review and merge workflows without external dashboards or manual status checking

vs alternatives

Simpler than building custom CI/CD evaluation pipelines because it provides pre-built GitHub Actions scaffolding, whereas generic evaluation tools require custom workflow orchestration and status reporting

multi-provider llm evaluation with configurable scoring rubrics

Medium confidence

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.

Solves for

Use my preferred LLM provider (OpenAI, Claude, open-source) for tool evaluation without code changesDefine custom evaluation criteria specific to my tool's domain or use caseSwitch LLM providers without rewriting evaluation logicOptimize 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

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

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

What makes it unique

Provider abstraction layer that normalizes evaluation across different LLM backends while preserving provider-specific capabilities, allowing users to define rubrics once and evaluate against OpenAI, Anthropic, or local models without code changes

vs alternatives

More flexible than single-provider evaluation tools because it decouples rubric definition from LLM choice, whereas alternatives like Anthropic's evaluation tools lock you into their provider ecosystem

tool call telemetry capture and structured logging

Medium confidence

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.

Solves for

Capture detailed logs of which tools were called and what they returned during test executionCorrelate tool calls with evaluation scores for debugging and analysisExport tool call telemetry for external analysis or archivalDetect 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

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

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

What makes it unique

MCP-native telemetry capture that understands tool schemas and call semantics, logging not just raw arguments but also semantic context like which tool was called and whether it succeeded, enabling evaluation systems to make informed scoring decisions

vs alternatives

More specialized than generic application logging because it captures MCP-specific metadata (tool definitions, call arguments, results) in a format directly consumable by evaluation systems, whereas generic logging requires custom parsing

regression detection via score trend analysis

Medium confidence

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.

Solves for

Automatically detect when tool evaluation scores drop compared to previous runsBlock PRs if evaluation scores regress below acceptable thresholdsVisualize score trends over time to identify patterns or systemic issuesAlert 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

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)

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

What makes it unique

Automated regression detection specifically for MCP tool evaluation scores, comparing current runs against historical baselines to identify quality degradation without manual threshold tuning or external monitoring systems

vs alternatives

More targeted than generic performance monitoring because it focuses on tool call quality metrics specific to MCP, whereas general monitoring tools require custom metric definition and alerting logic

evaluation result reporting and github integration

Medium confidence

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.

Solves for

See evaluation results directly in GitHub PR checks without visiting external dashboardsGet inline PR comments with evaluation feedback and scoresDownload detailed evaluation reports as workflow artifacts for archivalUse 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

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)

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

What makes it unique

Native GitHub Actions integration that automatically posts evaluation results as check runs and PR comments without requiring custom GitHub API orchestration, making results immediately visible in developers' existing GitHub workflows

vs alternatives

Simpler than building custom GitHub integrations because it provides pre-built reporting templates and GitHub API abstraction, whereas generic evaluation tools require manual GitHub API integration

configurable evaluation thresholds and pass/fail criteria

Medium confidence

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.

Solves for

Define what score constitutes a passing evaluation for my specific use caseSet different thresholds for different tool categories or criticality levelsFail CI/CD pipelines if evaluation scores don't meet minimum standardsAdjust 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

Requires

Configuration file or environment variables defining thresholds

Evaluation results in numeric format (scores, not just pass/fail)

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

What makes it unique

Flexible threshold configuration that allows per-tool or per-category scoring requirements, enabling teams to enforce different quality standards for different tool types without separate evaluation pipelines

vs alternatives

More granular than fixed pass/fail systems because it supports per-tool thresholds and weighted scoring, whereas simpler tools use one-size-fits-all thresholds

batch evaluation of multiple tool calls with aggregated scoring

Medium confidence

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.

Solves for

Evaluate all tool calls from a test run in one batch operationGet aggregate metrics like 'X% of tool calls passed' without evaluating each call separatelyIdentify which specific tools are failing most frequentlyReduce 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

Requires

Multiple tool calls to evaluate (minimum 2-3 for meaningful aggregation)

Batch evaluation configuration (batch size, aggregation method)

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

What makes it unique

Batch evaluation with per-tool aggregation that groups results by tool type, enabling teams to see not just overall pass rates but also which specific tools are underperforming without separate evaluation runs per tool

vs alternatives

More efficient than evaluating tool calls individually because it batches LLM API calls and aggregates results in one pass, whereas naive approaches evaluate each call separately with redundant API overhead

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with mcp-evals, ranked by overlap. Discovered automatically through the match graph.

MCP Server23

mcp-evals

GitHub Action for evaluating MCP server tool calls using LLM-based scoring

mcp server tool call evaluation via llm scoringllm-based tool call correctness scoring with structured rubricsgithub actions workflow integration for automated tool evaluation

3 shared capabilities

MCP Server28

mcp-bench

MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

multi-server tool-use benchmarking with complexity stratificationllm-as-judge multi-dimensional task evaluation with rule-based compliance scoring

2 shared capabilities

MCP Server24

Atla

** - Enable AI agents to interact with the [Atla API](https://docs.atla-ai.com/) for state-of-the-art LLMJ evaluation.

llm evaluation orchestration via mcp protocolmulti-metric llm output evaluation

2 shared capabilities

Platform40

Athina AI

LLM eval and monitoring with hallucination detection.

multi-provider llm integration for evaluationcustom evaluation metric builder with llm-as-judge

2 shared capabilities

Model44

langfuse

🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23

real-time llm-as-judge evaluation with configurable scoring rubrics

1 shared capability

Model43

opik

Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.

automated llm evaluation with multi-provider model support

1 shared capability

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
✓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
✓Teams with existing LLM provider relationships or cost constraints
✓Organizations with specific compliance requirements (e.g., on-premise models only)

Known 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
⚠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

Requirements

GitHub Actions workflow environmentMCP server implementation with tool definitionsAPI key for at least one LLM provider (OpenAI, Anthropic, etc.)Node.js 16+ or Python 3.8+ depending on implementationGitHub repository with Actions enabledGitHub Actions workflow file (.github/workflows/*.yml)Valid LLM API credentials stored as GitHub SecretsMCP server accessible or testable within GitHub Actions environment

Input / Output

Accepts: 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, GitHub Actions event triggers (push, pull_request, schedule), Workflow configuration (YAML), Test definitions or tool call scenarios, Tool call context (tool name, arguments, results, original request), Evaluation rubric (text prompt or structured template), Provider configuration (API key, model name, temperature, etc.), MCP tool invocation events (tool name, arguments, results), Execution context (request ID, timestamp, caller information), Current evaluation scores, Historical score data from previous runs, Regression threshold configuration, Evaluation results (scores, pass/fail, reasoning), GitHub context (commit SHA, PR number, branch), Evaluation scores (numeric), Threshold configuration (minimum score, pass/fail logic), Array of tool calls (tool name, arguments, results), Evaluation rubric applied to each call

Produces: Numeric scores (0-10 or 0-100 scale), Pass/fail verdicts, Structured evaluation reports (JSON), GitHub Actions check results with pass/fail status, GitHub check runs (pass/fail status), PR comments with evaluation summary, Workflow artifacts (JSON evaluation reports), GitHub Actions logs with detailed scoring breakdown, Numeric scores (normalized across providers), Structured evaluation results (JSON with score, reasoning, pass/fail), Provider-agnostic evaluation reports, Structured JSON logs with tool call details, Log files or streaming log output, Telemetry data for downstream evaluation systems, Regression alerts (pass/fail on regression check), Trend reports (JSON with score history and statistics), GitHub check results indicating regression status, GitHub check runs (visible in commit status), PR comments (visible in PR discussion), Workflow artifacts (downloadable JSON reports), Pass/fail verdict based on threshold comparison, Detailed pass/fail reasoning (which criteria passed/failed), 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')

UnfragileRank

Adoption70%(30% weight)

Quality17%(25% weight)

Ecosystem62%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

8 capabilities

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Repository Details

Package Details

npm

Registry

2.0.1

Version

153,720

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GitHub Action for evaluating MCP server tool calls using LLM-based scoring

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Capabilities8 decomposed

mcp server tool call evaluation via llm scoring

Medium confidence

Solves for

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

Requires

GitHub Actions workflow environment

MCP server implementation with tool definitions

API key for at least one LLM provider (OpenAI, Anthropic, etc.)

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

What makes it unique

vs alternatives

github actions workflow integration for automated test evaluation

Medium confidence

Solves for

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

Requires

GitHub repository with Actions enabled

GitHub Actions workflow file (.github/workflows/*.yml)

Valid LLM API credentials stored as GitHub Secrets

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)

What makes it unique

vs alternatives

multi-provider llm evaluation with configurable scoring rubrics

Medium confidence

Solves for

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

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

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

What makes it unique

vs alternatives

tool call telemetry capture and structured logging

Medium confidence

Solves for

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

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

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

What makes it unique

vs alternatives

regression detection via score trend analysis

Medium confidence

Solves for

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

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)

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

What makes it unique

vs alternatives

More targeted than generic performance monitoring because it focuses on tool call quality metrics specific to MCP, whereas general monitoring tools require custom metric definition and alerting logic

evaluation result reporting and github integration

Medium confidence

Solves for

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

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)

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

What makes it unique

vs alternatives

Simpler than building custom GitHub integrations because it provides pre-built reporting templates and GitHub API abstraction, whereas generic evaluation tools require manual GitHub API integration

configurable evaluation thresholds and pass/fail criteria

Medium confidence

Solves for

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

Requires

Configuration file or environment variables defining thresholds

Evaluation results in numeric format (scores, not just pass/fail)

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

What makes it unique

vs alternatives

More granular than fixed pass/fail systems because it supports per-tool thresholds and weighted scoring, whereas simpler tools use one-size-fits-all thresholds

batch evaluation of multiple tool calls with aggregated scoring

Medium confidence

Solves for

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

Requires

Multiple tool calls to evaluate (minimum 2-3 for meaningful aggregation)

Batch evaluation configuration (batch size, aggregation method)

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

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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mcp-evals

Capabilities8 decomposed

mcp server tool call evaluation via llm scoring

github actions workflow integration for automated test evaluation

multi-provider llm evaluation with configurable scoring rubrics

tool call telemetry capture and structured logging

regression detection via score trend analysis

evaluation result reporting and github integration

configurable evaluation thresholds and pass/fail criteria

batch evaluation of multiple tool calls with aggregated scoring

Related Artifactssharing capabilities

mcp-evals

mcp-bench

Atla

Athina AI

langfuse

opik

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

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Are you the builder of mcp-evals?

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mcp-evals

Capabilities8 decomposed

mcp server tool call evaluation via llm scoring

github actions workflow integration for automated test evaluation

multi-provider llm evaluation with configurable scoring rubrics

tool call telemetry capture and structured logging

regression detection via score trend analysis

evaluation result reporting and github integration

configurable evaluation thresholds and pass/fail criteria

batch evaluation of multiple tool calls with aggregated scoring

Related Artifactssharing capabilities

mcp-evals

mcp-bench

Atla

Athina AI

langfuse

opik

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to mcp-evals

Are you the builder of mcp-evals?

Get the weekly brief

Data Sources