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Agents can dynamically discover available evaluation tools through MCP's tool discovery mechanism and invoke them with structured parameters.","intents":["I want my AI agent to evaluate LLM outputs against custom metrics without writing HTTP client code","I need to integrate Atla evaluation into an agentic workflow that uses MCP-compatible tools","I want to expose evaluation capabilities to multiple agents through a standardized protocol"],"best_for":["AI agent developers building evaluation pipelines","Teams using Claude or other MCP-compatible LLM clients","Organizations standardizing on MCP for tool integration"],"limitations":["Requires MCP client support — not compatible with REST-only integrations","Evaluation latency depends on Atla API response times (typically 1-5 seconds per evaluation)","No built-in caching of evaluation results — each invocation hits the Atla API","Authentication via Atla API key must be provisioned in MCP server environment"],"requires":["Atla API key with evaluation permissions","MCP-compatible client (Claude, Cline, or custom MCP host)","Network access to Atla API endpoints","Node.js 18+ or Python 3.9+ (depending on MCP server implementation)"],"input_types":["structured evaluation parameters (JSON)","LLM output text","evaluation criteria/rubrics","reference answers or ground truth"],"output_types":["evaluation scores (numeric)","structured evaluation results (JSON)","detailed feedback/explanations","comparative metrics"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atla__cap_1","uri":"capability://data.processing.analysis.multi.metric.llm.output.evaluation","name":"multi-metric llm output evaluation","description":"Enables agents to evaluate LLM-generated text against multiple evaluation dimensions (correctness, relevance, coherence, factuality, etc.) through Atla's evaluation engine. The server translates agent requests into parameterized evaluation calls that invoke Atla's backend models or custom evaluation logic. Supports batch evaluation of multiple outputs against the same criteria and returns structured scores with optional explanations.","intents":["I want to evaluate whether my LLM's response is factually accurate and relevant to the user query","I need to compare multiple LLM outputs using consistent evaluation criteria","I want to measure specific quality dimensions (tone, clarity, completeness) of generated text"],"best_for":["LLM application developers building quality gates","Researchers comparing model outputs systematically","Teams implementing automated evaluation in CI/CD pipelines"],"limitations":["Evaluation quality depends on Atla's underlying models — custom metrics require Atla API support","No local evaluation — all requests go to Atla's cloud API (cannot run offline)","Batch evaluation throughput limited by Atla API rate limits","Evaluation criteria must be pre-defined or supported by Atla; arbitrary custom metrics not supported"],"requires":["Atla API key with evaluation access","LLM output text (string)","Evaluation criteria/metric names supported by Atla","Optional: reference answer or ground truth for comparative metrics"],"input_types":["text (LLM output)","text (user query or context)","text (reference answer)","structured evaluation parameters"],"output_types":["numeric scores (0-1 or 0-100 scale)","structured evaluation report (JSON)","textual explanations/reasoning","comparative rankings"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atla__cap_2","uri":"capability://planning.reasoning.agent.driven.evaluation.workflow.composition","name":"agent-driven evaluation workflow composition","description":"Allows AI agents to compose multi-step evaluation workflows by chaining evaluation calls with conditional logic. Agents can evaluate intermediate outputs, use results to decide next steps, and iteratively refine LLM responses based on evaluation feedback. The MCP server handles request routing and maintains evaluation context across multiple calls within a single agent session.","intents":["I want my agent to evaluate an LLM output, and if it fails quality checks, regenerate and re-evaluate","I need to run a multi-stage evaluation pipeline where later stages depend on earlier results","I want agents to autonomously improve outputs by evaluating and iterating"],"best_for":["Agentic systems implementing quality-driven loops","Teams building self-improving LLM pipelines","Developers creating evaluation-guided generation workflows"],"limitations":["Workflow latency compounds with each evaluation call (no parallelization built-in)","Agent must implement retry/iteration logic — MCP server is stateless","No built-in workflow persistence — agent session loss means workflow state is lost","Evaluation feedback must be explicitly parsed by agent; no automatic remediation"],"requires":["MCP-compatible agent with planning/reasoning capabilities","Atla API key","Agent implementation of iteration logic (e.g., Claude with tool use)"],"input_types":["LLM output text","evaluation criteria","conditional logic (agent-defined)","regeneration prompts"],"output_types":["evaluation scores at each stage","refined/improved outputs","workflow execution trace","final quality metrics"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atla__cap_3","uri":"capability://tool.use.integration.atla.api.credential.and.request.management","name":"atla api credential and request management","description":"Handles authentication, request signing, and API credential management for Atla API calls. The MCP server securely stores and injects Atla API keys into outbound requests, manages request/response serialization, and handles API errors with appropriate fallback behavior. Supports environment-based credential injection and secure credential rotation.","intents":["I want to securely pass my Atla API key to the MCP server without exposing it in agent prompts","I need the MCP server to handle API authentication transparently","I want to rotate API credentials without restarting the agent"],"best_for":["DevOps teams managing MCP server deployments","Organizations with credential rotation policies","Teams running MCP servers in shared environments"],"limitations":["Credentials stored in environment variables — not encrypted at rest","No built-in credential rotation mechanism — requires manual restart","API key exposed in server process memory (standard limitation)","No audit logging of API calls by default"],"requires":["Atla API key (provisioned via environment variable or config file)","Network access to Atla API endpoints","Secure environment for MCP server (not exposed to untrusted users)"],"input_types":["API key (string)","API endpoint URL"],"output_types":["authenticated API requests","error responses with status codes"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atla__cap_4","uri":"capability://memory.knowledge.evaluation.result.caching.and.deduplication","name":"evaluation result caching and deduplication","description":"Implements optional caching of evaluation results to avoid redundant API calls when the same LLM output is evaluated multiple times with identical criteria. The server maintains an in-memory cache keyed by output hash and evaluation parameters, returning cached results on subsequent identical requests. Supports cache invalidation and TTL-based expiration.","intents":["I want to avoid re-evaluating the same output multiple times in a single agent session","I need to reduce API costs by caching evaluation results","I want faster evaluation turnaround for repeated evaluations"],"best_for":["Cost-conscious teams running high-volume evaluations","Agents that may evaluate the same outputs multiple times","Development/testing environments with repeated evaluation patterns"],"limitations":["In-memory cache is lost on server restart — not persistent","Cache size unbounded by default — can cause memory bloat with many unique outputs","Cache hits only on exact parameter matches — no fuzzy matching","Stale cache may return outdated results if Atla's evaluation models are updated"],"requires":["MCP server with caching enabled (configuration flag)","Sufficient server memory for cache storage"],"input_types":["LLM output text","evaluation parameters"],"output_types":["cached evaluation results (if hit)","fresh evaluation results (if miss)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atla__cap_5","uri":"capability://tool.use.integration.tool.discovery.and.schema.exposure.via.mcp","name":"tool discovery and schema exposure via mcp","description":"Exposes Atla evaluation capabilities as discoverable MCP tools with full JSON schema definitions. The server implements MCP's tools/list and tools/call endpoints, allowing agents to dynamically discover available evaluation methods, their parameters, and return types. Schemas include parameter validation, required fields, and type constraints that agents can use for request construction.","intents":["I want my agent to discover what evaluation capabilities are available without hardcoding tool names","I need the agent to understand the parameters and return types of evaluation tools","I want type-safe tool invocation with schema validation"],"best_for":["Agents using dynamic tool discovery (Claude, Cline)","Teams building flexible agentic systems","Developers integrating Atla into MCP-based platforms"],"limitations":["Schema discovery adds latency on first agent connection (typically <100ms)","Schemas are static — cannot reflect dynamic evaluation methods created at runtime","Parameter validation happens at agent level — server does not validate before API call","Schema changes require server restart to propagate to agents"],"requires":["MCP client with tool discovery support","Atla API key"],"input_types":["MCP tool discovery requests"],"output_types":["JSON schema definitions","tool metadata (name, description, parameters)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atla__cap_6","uri":"capability://data.processing.analysis.batch.evaluation.request.handling","name":"batch evaluation request handling","description":"Supports evaluating multiple LLM outputs in a single request, allowing agents to evaluate different outputs or the same output against multiple criteria efficiently. The server batches requests to Atla's API where possible and returns results in a structured format that maps outputs to their evaluation scores. Handles partial failures gracefully, returning successful evaluations even if some requests fail.","intents":["I want to evaluate 10 different LLM outputs against the same criteria in one call","I need to evaluate one output against 5 different evaluation metrics simultaneously","I want to reduce API calls by batching multiple evaluations"],"best_for":["Batch evaluation workflows (e.g., evaluating model outputs from a dataset)","Comparative evaluation of multiple model variants","Cost-optimized evaluation pipelines"],"limitations":["Batch size limited by Atla API constraints (typically 10-100 items per batch)","Latency is determined by slowest item in batch (no early return)","Partial failures require agent-level retry logic for failed items","Batch requests cannot be parallelized across multiple API calls (sequential processing)"],"requires":["Multiple LLM outputs or evaluation criteria","Atla API key with batch evaluation support"],"input_types":["array of LLM outputs (text)","array of evaluation criteria","structured batch parameters"],"output_types":["array of evaluation results","structured batch response (JSON)","error details for failed items"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-atla__cap_7","uri":"capability://automation.workflow.error.handling.and.fallback.evaluation.strategies","name":"error handling and fallback evaluation strategies","description":"Implements graceful error handling for Atla API failures, including retry logic with exponential backoff, timeout handling, and fallback evaluation strategies. When Atla API is unavailable, the server can optionally fall back to lightweight heuristic-based evaluation or return cached results. Errors are surfaced to agents with structured error messages and retry recommendations.","intents":["I want my agent to handle Atla API outages without crashing","I need fallback evaluation when the API is slow or unavailable","I want clear error messages to help debug evaluation failures"],"best_for":["Production agentic systems requiring high availability","Teams with strict SLA requirements","Developers building resilient evaluation pipelines"],"limitations":["Fallback evaluation (heuristics) is less accurate than Atla's models","Retry logic adds latency (exponential backoff can take 10-30 seconds)","Cached fallback results may be stale if Atla models have been updated","No circuit breaker pattern — server will continue retrying even during extended outages"],"requires":["Atla API key","Network connectivity (for retries)"],"input_types":["evaluation requests","retry configuration (optional)"],"output_types":["evaluation results (from Atla or fallback)","error details with retry status","fallback indicator (if heuristic evaluation used)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Atla API key with evaluation permissions","MCP-compatible client (Claude, Cline, or custom MCP host)","Network access to Atla API endpoints","Node.js 18+ or Python 3.9+ (depending on MCP server implementation)","Atla API key with evaluation access","LLM output text (string)","Evaluation criteria/metric names supported by Atla","Optional: reference answer or ground truth for comparative metrics","MCP-compatible agent with planning/reasoning capabilities","Atla API key"],"failure_modes":["Requires MCP client support — not compatible with REST-only integrations","Evaluation latency depends on Atla API response times (typically 1-5 seconds per evaluation)","No built-in caching of evaluation results — each invocation hits the Atla API","Authentication via Atla API key must be provisioned in MCP server environment","Evaluation quality depends on Atla's underlying models — custom metrics require Atla API support","No local evaluation — all requests go to Atla's cloud API (cannot run offline)","Batch evaluation throughput limited by Atla API rate limits","Evaluation criteria must be pre-defined or supported by Atla; arbitrary custom metrics not supported","Workflow latency compounds with each evaluation call (no parallelization built-in)","Agent must implement retry/iteration logic — MCP server is stateless","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=atla","compare_url":"https://unfragile.ai/compare?artifact=atla"}},"signature":"Fk6kegwXLF7jupPQqwBcIjgEVhapzSGSwpM5jvr04S6+JfZOYW9V+y9c7Pwy+9Sx8cbCg6iMBdaHNZLwvyGjAQ==","signedAt":"2026-06-21T10:12:37.497Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/atla","artifact":"https://unfragile.ai/atla","verify":"https://unfragile.ai/api/v1/verify?slug=atla","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"}}