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Implements a search abstraction layer that normalizes query parameters and returns ranked results with relevance scoring, allowing developers to discover relevant datasets without manual catalog browsing.","intents":["Find datasets related to housing, transportation, or economic indicators without knowing exact dataset names","Discover all datasets published by a specific Singapore government agency","Search for datasets matching multiple criteria (e.g., 'population data from 2020-2024')","Integrate dataset discovery into an LLM agent workflow for automated data analysis"],"best_for":["Data analysts building automated reporting pipelines","LLM agents that need to autonomously discover and select datasets","Non-technical users querying government data through conversational interfaces"],"limitations":["Search results limited to datasets indexed by data.gov.sg API — no full-text search across dataset contents","Relevance ranking depends on metadata quality; poorly tagged datasets may not surface in results","No fuzzy matching or typo tolerance — exact term matching required for optimal results"],"requires":["MCP client implementation (Claude Desktop, custom MCP runner, or compatible LLM interface)","Network connectivity to data.gov.sg API endpoints","No authentication required — uses public API endpoints"],"input_types":["text (natural language search query)","structured filters (agency name, dataset type, date range)"],"output_types":["structured JSON (dataset metadata: name, description, agency, update frequency, format)","ranked result list with relevance scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aniruddha-adhikary-gahmen-mcp__cap_1","uri":"capability://data.processing.analysis.filtered.dataset.metadata.retrieval.with.schema.inspection","name":"filtered dataset metadata retrieval with schema inspection","description":"Fetches complete metadata for a specific dataset including schema information, column definitions, data types, and update frequency. Implements a metadata normalization layer that parses data.gov.sg's API responses and exposes structured schema details, enabling developers to understand dataset structure before download without inspecting raw files.","intents":["Inspect column names and data types before deciding to download a large dataset","Understand what fields are available in a dataset to construct appropriate analysis queries","Retrieve dataset update schedules and last-modified timestamps for freshness validation","Generate data validation schemas or type definitions from dataset metadata"],"best_for":["Data engineers validating dataset compatibility before ETL pipeline integration","LLM agents that need to understand data structure before generating analysis code","Developers building data catalog applications or metadata-driven UIs"],"limitations":["Schema information only available if dataset publisher provided it in data.gov.sg metadata — some datasets may have incomplete schema details","No sample data preview — only structural metadata, not actual row samples","Data type inference limited to what publisher declared; no automatic type detection from actual data"],"requires":["MCP client with tool-calling capability","Valid dataset identifier (dataset_id from search results)","Network access to data.gov.sg API"],"input_types":["text (dataset identifier/name)","optional filters (specific fields to retrieve)"],"output_types":["structured JSON (schema: column names, types, descriptions, constraints)","metadata object (update frequency, last modified, data format, file size)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aniruddha-adhikary-gahmen-mcp__cap_2","uri":"capability://data.processing.analysis.filtered.dataset.download.with.format.conversion.and.sampling","name":"filtered dataset download with format conversion and sampling","description":"Downloads datasets from data.gov.sg with support for multiple output formats (CSV, JSON, XML) and optional filtering/sampling to reduce payload size. Implements a download orchestration layer that handles format negotiation with the upstream API, applies client-side filtering predicates, and streams results to avoid memory exhaustion on large datasets.","intents":["Download a specific dataset in CSV format for local analysis","Fetch only rows matching certain criteria (e.g., 'all records from 2024') without downloading entire dataset","Convert dataset from XML to JSON format for easier programmatic processing","Sample a large dataset (e.g., first 1000 rows) for exploratory analysis before full download"],"best_for":["Data scientists downloading datasets for local analysis in Python/R","Automated pipelines that need to fetch and transform government data regularly","LLM agents that need to retrieve data subsets for analysis without overwhelming context windows"],"limitations":["Filtering applied client-side after download — no server-side query pushdown, so large datasets must be fully downloaded then filtered","Format conversion limited to formats supported by data.gov.sg API — not all datasets available in all formats","Sampling is random/sequential only — no stratified or weighted sampling options","No compression support — downloads are uncompressed, potentially large for multi-GB datasets"],"requires":["MCP client with file streaming or large response handling","Sufficient local disk space for downloaded dataset","Network bandwidth for potentially large file transfers","Dataset must be publicly accessible on data.gov.sg (no authentication-gated datasets)"],"input_types":["text (dataset identifier)","structured filters (column name, operator, value)","format specification (csv, json, xml)","optional sampling parameters (row count or percentage)"],"output_types":["binary/text file (CSV, JSON, or XML format)","structured data stream (for streaming consumption)","metadata about download (rows retrieved, bytes transferred, applied filters)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aniruddha-adhikary-gahmen-mcp__cap_3","uri":"capability://search.retrieval.dataset.collection.browsing.and.hierarchical.navigation","name":"dataset collection browsing and hierarchical navigation","description":"Exposes data.gov.sg's dataset collections (curated groupings by theme, agency, or domain) as navigable MCP tools, enabling developers to explore datasets hierarchically rather than through flat search. Implements a collection tree abstraction that maps data.gov.sg's organizational structure and allows drilling down from high-level themes (e.g., 'Economy') to specific datasets.","intents":["Browse all datasets published by a specific Singapore government ministry or agency","Explore datasets grouped by theme (e.g., 'Transportation', 'Health', 'Environment')","Discover related datasets within a collection without knowing exact names","Build a hierarchical data catalog UI that mirrors government data organization"],"best_for":["Data explorers and analysts unfamiliar with Singapore government data landscape","LLM agents that benefit from hierarchical context (e.g., 'show me all transportation datasets')","Developers building data discovery interfaces or knowledge graphs"],"limitations":["Collection structure is static/cached — reflects data.gov.sg's organization at last sync, not real-time changes","Some datasets may appear in multiple collections, creating ambiguity in hierarchical navigation","No custom collection creation — limited to data.gov.sg's pre-defined collections"],"requires":["MCP client with support for nested/hierarchical tool responses","Initial collection index cached or fetched from data.gov.sg"],"input_types":["text (collection name or theme)","optional depth parameter (how many levels to traverse)"],"output_types":["structured JSON (collection tree with nested datasets)","flat list of datasets within a collection","collection metadata (description, agency, update frequency)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aniruddha-adhikary-gahmen-mcp__cap_4","uri":"capability://automation.workflow.dataset.update.monitoring.and.freshness.tracking","name":"dataset update monitoring and freshness tracking","description":"Tracks dataset update schedules and last-modified timestamps, enabling developers to monitor data freshness and trigger downstream processes when datasets are updated. Implements a metadata polling abstraction that queries data.gov.sg for update information and exposes it as queryable MCP tools, allowing agents to make freshness-aware decisions about data usage.","intents":["Check if a dataset has been updated since the last time it was downloaded","Retrieve the update schedule for a dataset to understand data freshness expectations","Trigger automated analysis pipelines only when new data is available","Monitor multiple datasets and alert when any have been updated"],"best_for":["Automated data pipelines that need to refresh only when source data changes","LLM agents that should prioritize recent data over stale datasets","Data quality monitoring systems that track dataset staleness"],"limitations":["Update information depends on publisher metadata — some datasets may not have accurate update schedules","No real-time change detection — only periodic polling of data.gov.sg metadata","No granular change tracking (e.g., which rows changed) — only dataset-level freshness"],"requires":["MCP client with periodic polling capability (or external scheduler)","Persistent storage for tracking last-known update timestamps","Network access to data.gov.sg API"],"input_types":["text (dataset identifier)","optional time window (check updates since timestamp)"],"output_types":["structured JSON (last modified timestamp, update frequency, next expected update)","boolean (has dataset been updated since last check)","update history (list of recent updates with timestamps)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aniruddha-adhikary-gahmen-mcp__cap_5","uri":"capability://memory.knowledge.multi.dataset.correlation.and.relationship.discovery","name":"multi-dataset correlation and relationship discovery","description":"Analyzes metadata across multiple datasets to identify potential correlations, shared dimensions, and relationships (e.g., datasets sharing geographic regions, time periods, or entity types). Implements a metadata graph abstraction that builds connections between datasets based on common fields, enabling developers to discover complementary datasets for joint analysis.","intents":["Find datasets that can be joined on common fields (e.g., postal code, date range)","Identify datasets covering the same geographic region or time period for correlation analysis","Discover datasets from different agencies that measure related phenomena","Build a knowledge graph of dataset relationships for exploratory analysis"],"best_for":["Data scientists performing multi-dataset analysis and correlation studies","LLM agents that need to autonomously select complementary datasets","Researchers building comprehensive views across multiple government data sources"],"limitations":["Correlation detection limited to explicit metadata matches — no semantic understanding of field meanings","Requires datasets to have consistent naming conventions or explicit schema mappings","No validation that datasets are actually joinable — only metadata-level correlation","Relationship discovery is static/cached, not real-time"],"requires":["MCP client with graph traversal or relationship query support","Metadata index of all datasets with normalized field names","Optional: external knowledge base mapping field synonyms (e.g., 'postal_code' = 'zip_code')"],"input_types":["text (dataset identifier or field name)","optional correlation type (geographic, temporal, entity-based)"],"output_types":["structured JSON (related datasets with correlation type and strength)","graph representation (nodes=datasets, edges=relationships)","join recommendations (suggested join keys and compatibility)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aniruddha-adhikary-gahmen-mcp__cap_6","uri":"capability://memory.knowledge.agency.and.data.steward.information.retrieval","name":"agency and data steward information retrieval","description":"Retrieves metadata about data-publishing agencies, stewards, and contact information from data.gov.sg, enabling developers to understand data provenance and reach out to publishers for clarifications. Implements an agency directory abstraction that maps Singapore government organizational structure and exposes steward contact details and data governance policies.","intents":["Find contact information for the agency responsible for a dataset","Understand which government ministry publishes a specific dataset","Retrieve data governance policies or usage terms for a dataset","Identify the primary data steward for a dataset for clarification requests"],"best_for":["Data analysts needing to contact publishers for data clarifications","Compliance teams tracking data provenance and governance","LLM agents that need to cite data sources and stewards in reports"],"limitations":["Contact information may be outdated or generic (e.g., shared agency email)","No direct contact with individual data stewards — only agency-level contacts","Governance policies may not be machine-readable or standardized across agencies"],"requires":["MCP client with text/structured data retrieval","Access to data.gov.sg's agency directory metadata"],"input_types":["text (dataset identifier or agency name)"],"output_types":["structured JSON (agency name, ministry, contact email, website)","text (data governance policies, usage terms, attribution requirements)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aniruddha-adhikary-gahmen-mcp__cap_7","uri":"capability://data.processing.analysis.dataset.usage.statistics.and.popularity.metrics","name":"dataset usage statistics and popularity metrics","description":"Retrieves download counts, view statistics, and popularity metrics for datasets from data.gov.sg, enabling developers to identify widely-used datasets and understand data consumption patterns. Implements a metrics aggregation layer that normalizes usage data across datasets and exposes it as queryable MCP tools.","intents":["Find the most popular or frequently-downloaded datasets in a category","Understand how widely a dataset is used to assess its reliability and maturity","Identify trending datasets that are gaining traction","Prioritize dataset analysis based on community adoption and usage"],"best_for":["Data analysts selecting datasets based on community adoption","LLM agents that should prioritize popular, well-tested datasets","Researchers studying data consumption patterns in Singapore government"],"limitations":["Usage statistics may not be available for all datasets — only those with tracking enabled","Metrics may be aggregated/anonymized and not granular","No breakdown of usage by use case or user type","Historical usage trends may not be available"],"requires":["MCP client with numeric/metric data retrieval","Access to data.gov.sg's usage analytics API or metadata"],"input_types":["text (dataset identifier or category)","optional time window (usage in last N days/months)"],"output_types":["structured JSON (download count, view count, last accessed timestamp)","ranked list (datasets sorted by popularity metric)","trend data (usage over time)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (Claude Desktop, custom MCP runner, or compatible LLM interface)","Network connectivity to data.gov.sg API endpoints","No authentication required — uses public API endpoints","MCP client with tool-calling capability","Valid dataset identifier (dataset_id from search results)","Network access to data.gov.sg API","MCP client with file streaming or large response handling","Sufficient local disk space for downloaded dataset","Network bandwidth for potentially large file transfers","Dataset must be publicly accessible on data.gov.sg (no authentication-gated datasets)"],"failure_modes":["Search results limited to datasets indexed by data.gov.sg API — no full-text search across dataset contents","Relevance ranking depends on metadata quality; poorly tagged datasets may not surface in results","No fuzzy matching or typo tolerance — exact term matching required for optimal results","Schema information only available if dataset publisher provided it in data.gov.sg metadata — some datasets may have incomplete schema details","No sample data preview — only structural metadata, not actual row samples","Data type inference limited to what publisher declared; no automatic type detection from actual data","Filtering applied client-side after download — no server-side query pushdown, so large datasets must be fully downloaded then filtered","Format conversion limited to formats supported by data.gov.sg API — not all datasets available in all formats","Sampling is random/sequential only — no stratified or weighted sampling options","No compression support — downloads are uncompressed, potentially large for multi-GB datasets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8652019171128074,"quality":0.41,"ecosystem":0.6900000000000001,"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":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:18:25.565Z","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=aniruddha-adhikary-gahmen-mcp","compare_url":"https://unfragile.ai/compare?artifact=aniruddha-adhikary-gahmen-mcp"}},"signature":"ABgRh/weeYpuTjbYNN9ofTBmHnq4rGsuGuXgf2SaLn3V2WDhn6gA39CXVRBLISc0lfOO2JLaraNgQVBuJGquBQ==","signedAt":"2026-06-20T06:21:46.993Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aniruddha-adhikary-gahmen-mcp","artifact":"https://unfragile.ai/aniruddha-adhikary-gahmen-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=aniruddha-adhikary-gahmen-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"}}