{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_adityasaravana-caltrain-mcp","slug":"adityasaravana-caltrain-mcp","name":"Caltrain","type":"mcp","url":"https://github.com/adityasaravana/caltrain-mcp","page_url":"https://unfragile.ai/adityasaravana-caltrain-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:adityasaravana/caltrain-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_adityasaravana-caltrain-mcp__cap_0","uri":"capability://tool.use.integration.real.time.transit.arrival.prediction.via.gtfs.data.integration","name":"real-time transit arrival prediction via gtfs data integration","description":"Fetches live Caltrain schedule data from official GTFS (General Transit Feed Specification) feeds and exposes arrival predictions through MCP tool calls. The server parses GTFS static schedules and real-time updates, matching user queries (station names, routes) against the transit database to return next departure times and platform information. Integration happens via MCP's standardized tool-calling interface, allowing Claude and other LLM clients to invoke transit queries as native function calls without custom HTTP handling.","intents":["Query the next Caltrain arrival at a specific station without leaving my AI conversation","Build an AI agent that can answer commute planning questions with live transit data","Integrate real-time Bay Area transit information into an LLM-powered chatbot or assistant"],"best_for":["AI developers building Bay Area transit-aware agents","Teams integrating MCP servers into Claude Desktop or other LLM clients","Builders prototyping location-aware AI assistants for commute planning"],"limitations":["Limited to Caltrain service area only — no other Bay Area transit systems (BART, Muni, VTA)","GTFS data freshness depends on Caltrain's update frequency; real-time accuracy subject to official feed delays","No historical schedule analysis or predictive delay modeling beyond official GTFS data","Requires network access to Caltrain GTFS endpoints; no offline fallback"],"requires":["MCP-compatible client (Claude Desktop, or custom MCP host)","Network connectivity to Caltrain GTFS data sources","Node.js runtime (inferred from MCP server architecture)"],"input_types":["natural language station queries (e.g., 'next train from Palo Alto')","structured tool parameters (station name, optional route filter)"],"output_types":["structured JSON with arrival times, platform numbers, and route identifiers","human-readable text summaries formatted for LLM context"],"categories":["tool-use-integration","search-retrieval","transit-data"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_adityasaravana-caltrain-mcp__cap_1","uri":"capability://tool.use.integration.mcp.tool.schema.exposure.for.transit.queries","name":"mcp tool schema exposure for transit queries","description":"Exposes Caltrain transit queries as standardized MCP tools with JSON schema definitions, enabling Claude and other MCP-compatible clients to discover, understand, and invoke transit lookups through the protocol's native tool-calling mechanism. The server defines tool schemas (input parameters like station name, output structure with arrival times) that the MCP client parses and presents to the LLM, allowing the LLM to autonomously decide when to call transit functions without explicit prompting.","intents":["Enable Claude to autonomously call transit functions when users ask about commute times","Expose transit data as discoverable tools in MCP-compatible IDEs and clients","Allow LLM agents to chain transit queries with other tools (calendar, mapping, etc.) for multi-step planning"],"best_for":["LLM application developers using Claude Desktop or MCP-compatible hosts","Teams building multi-tool AI agents where transit is one capability among many","Developers familiar with MCP protocol and tool schema patterns"],"limitations":["Tool schema must be manually maintained if Caltrain GTFS structure changes","No dynamic schema generation from GTFS metadata — schemas are hardcoded","Limited to tools that MCP protocol supports; no streaming or long-running query patterns","Client-side tool discovery depends on MCP server initialization; no lazy-loading of tool definitions"],"requires":["MCP-compatible client with tool-calling support (Claude Desktop 0.2.0+, or custom MCP host)","Understanding of MCP tool schema format (JSON Schema subset)","Node.js runtime for the MCP server"],"input_types":["JSON-formatted tool parameters (station name as string, optional filters)"],"output_types":["JSON-structured tool results with arrival times, routes, and metadata","MCP protocol messages (CallToolResult with content and optional error fields)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_adityasaravana-caltrain-mcp__cap_2","uri":"capability://data.processing.analysis.gtfs.static.schedule.parsing.and.indexing","name":"gtfs static schedule parsing and indexing","description":"Parses official Caltrain GTFS static feed files (stops.txt, stop_times.txt, routes.txt, calendar.txt) into an in-memory index structure for fast station and route lookups. The server builds a queryable data structure mapping station names to stop IDs, routes to trip patterns, and schedules to calendar dates, enabling sub-millisecond response times for arrival queries without repeated file I/O or external database calls.","intents":["Quickly resolve user-provided station names to canonical GTFS stop IDs","Filter schedules by route, direction, and service calendar (weekday vs weekend)","Support fuzzy or partial station name matching for user convenience"],"best_for":["Developers building transit agents that need sub-100ms query latency","Teams operating MCP servers with memory constraints (indexing trades memory for speed)","Applications requiring offline-first transit lookups after initial GTFS download"],"limitations":["In-memory index requires full GTFS dataset to be loaded at server startup; no lazy-loading","Memory footprint grows with GTFS file size; Caltrain's full dataset is ~5-10MB, limiting scalability to larger transit agencies","Index is rebuilt on every server restart; no persistence layer for incremental updates","No support for real-time service alerts or schedule modifications beyond static GTFS"],"requires":["GTFS feed files accessible at server startup (local files or HTTP URLs)","Sufficient RAM to hold parsed GTFS index (~50-100MB for typical regional transit agency)","Node.js with standard library support for file I/O and data structures"],"input_types":["GTFS CSV files (stops.txt, stop_times.txt, routes.txt, calendar.txt, etc.)","Station name queries (natural language or partial matches)"],"output_types":["Indexed data structures (maps, arrays) for in-memory lookups","Resolved stop IDs, route information, and schedule arrays"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_adityasaravana-caltrain-mcp__cap_3","uri":"capability://search.retrieval.station.name.resolution.with.fuzzy.matching","name":"station name resolution with fuzzy matching","description":"Resolves user-provided station names (which may be partial, misspelled, or colloquial) to canonical Caltrain stop IDs by applying fuzzy string matching algorithms (likely Levenshtein distance or similar) against the indexed GTFS stops database. This allows users to query 'Palo Alto' or 'PA' and reliably get results for the official 'Palo Alto Caltrain Station' stop, improving usability in conversational contexts where exact names aren't guaranteed.","intents":["Handle user queries with station name typos or abbreviations without failing","Support colloquial station names (e.g., 'downtown SF' → 'San Francisco Caltrain')","Provide helpful suggestions when an exact match isn't found"],"best_for":["Conversational AI agents where users may not know exact station names","Mobile or voice-based interfaces where typing precision is low","International or non-English-speaking users unfamiliar with official Caltrain nomenclature"],"limitations":["Fuzzy matching may produce false positives for similarly-named stations (e.g., 'San Francisco' vs 'San Francisco Caltrain')","No support for multi-language station names or transliteration","Fuzzy matching threshold is likely hardcoded; no user-tunable confidence levels","Performance degrades with large station lists (though Caltrain's ~30 stations is negligible)"],"requires":["Fuzzy matching library (e.g., fuse.js, string-similarity, or custom Levenshtein implementation)","Pre-indexed GTFS stops database with canonical names"],"input_types":["user-provided station name string (any case, with potential typos)"],"output_types":["matched stop ID with confidence score","list of candidate matches if ambiguous"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_adityasaravana-caltrain-mcp__cap_4","uri":"capability://tool.use.integration.mcp.server.lifecycle.management.and.smithery.deployment.compatibility","name":"mcp server lifecycle management and smithery deployment compatibility","description":"Implements the MCP server protocol lifecycle (initialization, tool discovery, request handling, graceful shutdown) and is compatible with Smithery's MCP server registry and deployment infrastructure. The server handles MCP protocol messages (Initialize, CallTool, etc.), manages resource cleanup, and exposes metadata (name, version, capabilities) that Smithery uses to list and instantiate the server in its marketplace.","intents":["Deploy the Caltrain MCP server to Smithery for one-click installation by other users","Ensure the server properly initializes and shuts down in containerized MCP host environments","Expose server capabilities and metadata for discovery in MCP client registries"],"best_for":["Developers publishing MCP servers to Smithery or other registries","Teams running MCP servers in managed hosting environments (Smithery, Replit, etc.)","Users installing pre-built MCP servers without manual configuration"],"limitations":["Smithery deployment requires adherence to specific manifest format and metadata requirements","No built-in support for server versioning or rolling updates; Smithery handles version management","Server lifecycle is tied to the MCP host process; no independent persistence or recovery","Smithery compatibility may break if Smithery changes its MCP server interface or registry format"],"requires":["Smithery account and registry access (for publishing)","MCP-compatible host environment (Claude Desktop, Smithery, or custom MCP runner)","Node.js runtime with MCP SDK/library"],"input_types":["MCP protocol messages (JSON-RPC format)"],"output_types":["MCP protocol responses (Initialize, Tool results, errors)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude Desktop, or custom MCP host)","Network connectivity to Caltrain GTFS data sources","Node.js runtime (inferred from MCP server architecture)","MCP-compatible client with tool-calling support (Claude Desktop 0.2.0+, or custom MCP host)","Understanding of MCP tool schema format (JSON Schema subset)","Node.js runtime for the MCP server","GTFS feed files accessible at server startup (local files or HTTP URLs)","Sufficient RAM to hold parsed GTFS index (~50-100MB for typical regional transit agency)","Node.js with standard library support for file I/O and data structures","Fuzzy matching library (e.g., fuse.js, string-similarity, or custom Levenshtein implementation)"],"failure_modes":["Limited to Caltrain service area only — no other Bay Area transit systems (BART, Muni, VTA)","GTFS data freshness depends on Caltrain's update frequency; real-time accuracy subject to official feed delays","No historical schedule analysis or predictive delay modeling beyond official GTFS data","Requires network access to Caltrain GTFS endpoints; no offline fallback","Tool schema must be manually maintained if Caltrain GTFS structure changes","No dynamic schema generation from GTFS metadata — schemas are hardcoded","Limited to tools that MCP protocol supports; no streaming or long-running query patterns","Client-side tool discovery depends on MCP server initialization; no lazy-loading of tool definitions","In-memory index requires full GTFS dataset to be loaded at server startup; no lazy-loading","Memory footprint grows with GTFS file size; Caltrain's full dataset is ~5-10MB, limiting scalability to larger transit agencies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:25.062Z","last_scraped_at":"2026-05-03T15:19:46.450Z","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=adityasaravana-caltrain-mcp","compare_url":"https://unfragile.ai/compare?artifact=adityasaravana-caltrain-mcp"}},"signature":"gThPef9wvzve2XhzPMehgS/0zWhp/WrdsGtQJTkbe+Kt7KyZcffUFUdIBotGrQfC7DkvWzdyTPRrRNFKLK7rAA==","signedAt":"2026-06-21T19:49:22.348Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/adityasaravana-caltrain-mcp","artifact":"https://unfragile.ai/adityasaravana-caltrain-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=adityasaravana-caltrain-mcp","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"}}