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Implements request routing through MCP's tool-calling schema, translating conversational queries into structured API calls to PlainSignal's backend, with response marshaling back into LLM-consumable formats. 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Each metric (traffic, users, conversion rate, etc.) is registered as a callable tool with parameters for time ranges, filters, and aggregation dimensions. Implements tool discovery so MCP clients can introspect available analytics capabilities, their required/optional parameters, and expected output formats without external documentation.","intents":["Allow LLM agents to discover what analytics metrics are available without reading external docs","Enable type-safe parameter passing for analytics queries (date validation, filter syntax)","Provide clear documentation for each metric so agents understand what data they're querying","Support dynamic tool composition where agents chain multiple analytics queries together"],"best_for":["Developers building self-documenting analytics interfaces","Teams using Claude or other LLMs that leverage tool schemas for planning","Organizations standardizing analytics query patterns across multiple applications"],"limitations":["Schema must be manually maintained when PlainSignal API adds new metrics or changes parameters","Complex nested filters may require verbose schema definitions that reduce readability","Tool discovery adds overhead to initial MCP connection — not suitable for extremely latency-sensitive applications","Schema validation is client-side; server must still validate all parameters at query time"],"requires":["PlainSignal API documentation to define metric schemas","MCP SDK with tool schema support","JSON Schema or equivalent type definition capability"],"input_types":["metric names (strings)","filter parameters (dates, user segments, dimensions)","aggregation specifications"],"output_types":["tool schema definitions (JSON Schema format)","metric documentation (text descriptions)","parameter validation rules"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-plainsignal__cap_3","uri":"capability://text.generation.language.conversational.query.translation.to.structured.analytics.requests","name":"conversational query translation to structured analytics requests","description":"Enables LLM agents to express analytics queries in natural language (e.g., 'show me traffic from the US last week') and translates them into structured PlainSignal API calls with proper parameters. Works through the MCP tool-calling interface where the LLM agent decides which analytics tool to invoke and with what parameters; the MCP server validates and executes the translated request. Supports multi-turn conversations where follow-up queries can reference previous results or refine filters.","intents":["Ask analytics questions in natural language without learning API syntax","Refine analytics queries iteratively through conversation (e.g., 'now filter to mobile users')","Build analytics workflows where agents combine multiple queries to answer complex questions","Enable non-technical users to explore website data through conversational interfaces"],"best_for":["Non-technical stakeholders exploring analytics through conversational AI","Developers building analytics chatbots or dashboards","Teams automating analytics reporting through LLM agents"],"limitations":["Translation quality depends on LLM's understanding of analytics concepts — ambiguous queries may be misinterpreted","No explicit query planning or optimization — LLM may request redundant or inefficient metric combinations","Context window limitations mean long conversation histories may lose earlier query context","Requires LLM to understand PlainSignal's specific metric names and filter syntax through tool schemas"],"requires":["LLM with tool-calling capability (Claude, GPT-4, etc.)","MCP client that relays tool calls to the PlainSignal MCP server","Well-defined tool schemas so the LLM understands available metrics"],"input_types":["natural language queries (conversational text)"],"output_types":["structured analytics data","formatted summaries of results","follow-up suggestions for refinement"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-plainsignal__cap_4","uri":"capability://data.processing.analysis.real.time.data.synchronization.and.caching.strategy","name":"real-time data synchronization and caching strategy","description":"Manages the flow of real-time analytics data from PlainSignal's API to MCP clients, with optional caching to reduce API call frequency and latency. Implements request deduplication (if multiple clients query the same metric within a time window, reuse the cached result) and cache invalidation strategies (time-based TTL, event-based invalidation). Handles the trade-off between data freshness and API rate limits, allowing configuration of cache duration per metric type.","intents":["Reduce API calls to PlainSignal by caching frequently-requested metrics","Serve analytics data with minimal latency by avoiding redundant API requests","Handle rate limiting gracefully by batching requests and respecting API quotas","Support multiple concurrent LLM agents querying analytics without overwhelming the backend"],"best_for":["Teams with high-volume analytics queries that risk hitting rate limits","Applications requiring sub-second response times for analytics","Deployments with multiple LLM agents sharing a single PlainSignal account"],"limitations":["Caching introduces data staleness — real-time metrics may be 30-60 seconds old depending on TTL","In-memory caching doesn't persist across server restarts — requires external cache store (Redis) for production","Cache invalidation logic must be manually tuned per metric; incorrect TTLs can serve stale data","No built-in cache warming or predictive prefetching — cache hits depend on query patterns"],"requires":["In-memory cache (built-in) or external cache store like Redis for distributed deployments","Configuration mechanism to set cache TTLs per metric","Monitoring to track cache hit rates and API call volume"],"input_types":["analytics queries with cache parameters"],"output_types":["cached or fresh analytics data","cache metadata (age, hit/miss status)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["PlainSignal API key and account credentials","MCP client implementation (Claude Desktop, custom MCP host, or compatible LLM framework)","Node.js 16+ or Python 3.8+ (depending on MCP server implementation language)","Network access to PlainSignal API endpoints","MCP SDK for the target language (Node.js/TypeScript or Python)","PlainSignal API credentials (API key)","Process manager or container orchestration for production deployment","MCP client that supports the server's protocol version","PlainSignal API documentation to define metric schemas","MCP SDK with tool schema support"],"failure_modes":["Requires active PlainSignal account and API credentials — no free tier analytics access","Query latency depends on PlainSignal backend response times; real-time data may have 1-5 minute lag","Limited to metrics exposed by PlainSignal API — cannot query custom events or dimensions not supported by the platform","MCP protocol overhead adds ~50-100ms per request compared to direct REST API calls","No built-in caching or query result persistence — each conversation turn hits the API fresh","MCP server must be running continuously — no serverless/stateless deployment without external process manager","Single-threaded request handling in basic implementations may bottleneck under high concurrent query load","No built-in load balancing or horizontal scaling — requires external orchestration for multi-instance deployments","MCP protocol version compatibility must be maintained as the spec evolves","Schema must be manually maintained when PlainSignal API adds new metrics or changes parameters","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"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":"2026-06-17T09:51:04.047Z","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=plainsignal","compare_url":"https://unfragile.ai/compare?artifact=plainsignal"}},"signature":"lbU/IgbD1qxauerfdk+dlo0ZmE4qo7gfDxenpy14vcjcdeYYVo+vS+S/aZUherFLI3GUzn93RGre8jHOrCRHAQ==","signedAt":"2026-06-20T08:20:54.705Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/plainsignal","artifact":"https://unfragile.ai/plainsignal","verify":"https://unfragile.ai/api/v1/verify?slug=plainsignal","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"}}