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This allows non-SQL-fluent users to explore metrics, logs, and time-series data through conversational interfaces without writing raw SQL.","intents":["I want to ask my AI assistant about CPU usage trends over the last week without writing SQL","Enable my chatbot to answer questions about application performance metrics from GreptimeDB","Let users explore time-series data through natural language without database expertise"],"best_for":["AI application developers building observability chatbots","Teams integrating LLM assistants with time-series monitoring systems","Non-technical users querying metrics through AI interfaces"],"limitations":["Query translation accuracy depends on LLM's SQL generation capability — complex aggregations may require refinement","No built-in query optimization — relies on GreptimeDB's query planner for performance","Context window limitations may prevent analysis of very large result sets in a single conversation"],"requires":["GreptimeDB instance running and accessible","MCP client implementation (Claude, other LLM platforms with MCP support)","Network connectivity between MCP server and GreptimeDB"],"input_types":["natural language queries","conversational context"],"output_types":["structured query results (JSON/tabular)","query metadata (schema, row counts)"],"categories":["tool-use-integration","natural-language-to-sql"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-greptimedb__cap_1","uri":"capability://memory.knowledge.schema.introspection.and.table.discovery","name":"schema introspection and table discovery","description":"Provides AI assistants with real-time access to GreptimeDB schema metadata including table names, column definitions, data types, and temporal properties. The MCP server exposes schema discovery endpoints that return structured metadata, allowing LLMs to understand available data before constructing queries. This enables context-aware query suggestions and prevents invalid column references.","intents":["I want my AI to know what tables and columns exist before suggesting queries","Enable autocomplete and schema validation in AI-assisted query building","Let the assistant understand data types to suggest appropriate aggregations"],"best_for":["Developers building intelligent query builders","Teams implementing AI-powered data exploration interfaces","Organizations with dynamic schemas requiring real-time metadata sync"],"limitations":["Schema changes require re-fetching metadata — no built-in change notifications","Large schemas (1000+ tables) may incur latency in metadata retrieval","No schema versioning or historical schema tracking"],"requires":["GreptimeDB instance with accessible information_schema","MCP server with schema query permissions","Read-only database credentials"],"input_types":["schema query requests"],"output_types":["JSON schema definitions","table/column metadata","data type information"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-greptimedb__cap_2","uri":"capability://safety.moderation.secure.parameterized.query.execution.with.access.control","name":"secure parameterized query execution with access control","description":"Executes SQL queries against GreptimeDB through a controlled MCP interface that enforces parameterization, prevents SQL injection, and applies role-based access controls. The server validates query structure before execution, binds parameters safely, and enforces query timeouts and result limits. This allows AI assistants to run queries without exposing raw database credentials or enabling malicious operations.","intents":["I need my AI assistant to query the database safely without exposing credentials","Prevent SQL injection attacks when LLMs generate queries","Enforce query limits and timeouts to prevent resource exhaustion"],"best_for":["Production environments requiring secure AI-database integration","Multi-tenant systems where data isolation is critical","Organizations with strict security and compliance requirements"],"limitations":["Parameterization overhead adds ~5-10ms per query","Query timeout enforcement may interrupt long-running analytical queries","Access control granularity limited to table/role level — no row-level security integration"],"requires":["GreptimeDB with authentication enabled","MCP server running with restricted database credentials","TLS/SSL for MCP communication channel"],"input_types":["parameterized SQL queries","parameter bindings"],"output_types":["query results","execution metadata (rows affected, duration)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-greptimedb__cap_3","uri":"capability://data.processing.analysis.time.series.data.aggregation.and.downsampling","name":"time-series data aggregation and downsampling","description":"Enables AI assistants to request pre-aggregated or downsampled time-series data through high-level MCP operations that abstract GreptimeDB's aggregation functions. The server translates requests like 'hourly average' or 'daily max' into appropriate SQL GROUP BY and window function calls, returning reduced datasets suitable for visualization and analysis. This reduces data transfer and computation by leveraging GreptimeDB's native time-bucketing capabilities.","intents":["I want to get hourly averages of metrics without writing GROUP BY queries","Reduce data volume by requesting downsampled time-series for visualization","Enable the AI to suggest appropriate aggregation granularities based on time ranges"],"best_for":["Developers building observability dashboards with AI assistance","Teams analyzing large time-series datasets with performance constraints","Applications requiring real-time metric aggregation"],"limitations":["Aggregation accuracy depends on data distribution — sparse data may produce misleading averages","Pre-defined aggregation functions (avg, max, min, sum) — custom aggregations require SQL","Downsampling is lossy — original granularity data cannot be recovered"],"requires":["GreptimeDB with time-bucketing functions (DATE_TRUNC, DATE_BIN)","Time column explicitly defined in table schema","Sufficient query execution permissions"],"input_types":["aggregation type (avg, max, min, sum, count)","time bucket size (1m, 5m, 1h, 1d)","time range filters"],"output_types":["aggregated time-series data","bucket metadata (bucket size, count of values)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-greptimedb__cap_4","uri":"capability://data.processing.analysis.multi.table.join.and.correlation.analysis","name":"multi-table join and correlation analysis","description":"Allows AI assistants to correlate data across multiple GreptimeDB tables through MCP-exposed join operations that handle time-series alignment and temporal matching. The server constructs JOIN queries with automatic time-window alignment, preventing common pitfalls like mismatched timestamps or timezone issues. This enables analysis like 'correlate CPU usage with memory pressure' across separate metric tables.","intents":["I want to correlate metrics from different tables without manually writing JOINs","Enable the AI to find relationships between different time-series datasets","Analyze how changes in one metric affect another across aligned time windows"],"best_for":["Observability teams analyzing system behavior across multiple metric sources","Data analysts building correlation studies with AI assistance","Teams investigating root causes requiring cross-metric analysis"],"limitations":["Join performance degrades with large time windows or high cardinality keys","Timezone handling requires explicit configuration — automatic timezone conversion not supported","No built-in statistical correlation functions — requires post-processing for correlation coefficients"],"requires":["Multiple GreptimeDB tables with compatible time columns","Common join keys or time-window overlap","Sufficient query execution permissions on all tables"],"input_types":["table names","join keys","time-window specifications"],"output_types":["joined result sets","correlation metadata"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-greptimedb__cap_5","uri":"capability://data.processing.analysis.query.result.streaming.and.pagination","name":"query result streaming and pagination","description":"Streams large query result sets from GreptimeDB through the MCP protocol in paginated chunks, preventing memory exhaustion in the LLM context and enabling progressive analysis. The server implements cursor-based pagination with configurable page sizes, allowing assistants to fetch results incrementally and request additional pages on demand. This is critical for time-series queries that may return millions of rows.","intents":["I need to analyze large result sets without loading everything into memory","Enable the AI to progressively explore data by requesting additional pages","Prevent context window overflow when returning large query results"],"best_for":["Applications querying large time-series datasets","Developers building streaming data analysis interfaces","Teams with memory-constrained LLM deployments"],"limitations":["Cursor state must be maintained between requests — stateless deployments require external cursor storage","Pagination overhead adds latency for sequential page requests","Result ordering must be deterministic for cursor consistency — ORDER BY required"],"requires":["MCP client supporting streaming/pagination protocol","Cursor state management (in-memory or external store)","Deterministic query ordering"],"input_types":["page size","cursor token"],"output_types":["paginated result chunks","cursor tokens for next page","result metadata (total count, has_more)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-greptimedb__cap_6","uri":"capability://planning.reasoning.query.performance.analysis.and.optimization.suggestions","name":"query performance analysis and optimization suggestions","description":"Analyzes GreptimeDB query execution plans and provides AI-friendly optimization suggestions through MCP operations that expose query metrics like execution time, rows scanned, and index usage. The server extracts EXPLAIN PLAN output and translates it into natural language recommendations (e.g., 'add index on timestamp column', 'reduce time range to improve performance'). This enables assistants to suggest query optimizations without requiring deep database expertise.","intents":["I want the AI to explain why a query is slow and suggest improvements","Enable performance-aware query recommendations based on execution plans","Help users understand query costs before executing expensive operations"],"best_for":["Developers optimizing time-series query performance","Teams building AI-powered query advisors","Organizations monitoring database performance through AI interfaces"],"limitations":["EXPLAIN PLAN interpretation is heuristic-based — may miss subtle optimization opportunities","Index recommendations require schema modification permissions","Performance suggestions are generic — workload-specific tuning requires manual intervention"],"requires":["GreptimeDB with EXPLAIN PLAN support","Query execution permissions","Access to system statistics and index metadata"],"input_types":["SQL queries"],"output_types":["execution plan metadata","performance metrics (duration, rows scanned)","optimization suggestions (natural language)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-greptimedb__cap_7","uri":"capability://memory.knowledge.data.retention.and.ttl.policy.enforcement","name":"data retention and ttl policy enforcement","description":"Exposes GreptimeDB's data retention and time-to-live (TTL) policies through MCP operations, allowing AI assistants to understand data availability windows and warn users about data that may be deleted. The server queries table-level TTL configurations and retention policies, enabling assistants to suggest appropriate time ranges for analysis and alert when requested data may be outside retention windows.","intents":["I want the AI to warn me if I'm querying data that's about to be deleted","Enable the assistant to suggest valid time ranges based on data retention policies","Help users understand data lifecycle and archival schedules"],"best_for":["Teams managing large time-series datasets with aggressive retention policies","Developers building data exploration tools with lifecycle awareness","Organizations with compliance requirements around data retention"],"limitations":["TTL enforcement is asynchronous — deleted data may still be queryable briefly after TTL expiration","No built-in archival integration — only warns about deletion, doesn't suggest alternatives","Retention policies are table-level — no per-row or per-partition granularity"],"requires":["GreptimeDB tables with TTL policies configured","Access to system metadata tables","Read permissions on retention policy definitions"],"input_types":["table names"],"output_types":["TTL configuration metadata","data availability windows","retention policy descriptions"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-greptimedb__cap_8","uri":"capability://memory.knowledge.metric.metadata.and.semantic.tagging","name":"metric metadata and semantic tagging","description":"Manages and exposes semantic metadata about metrics stored in GreptimeDB, including descriptions, units, and custom tags, through MCP operations. The server stores and retrieves metric annotations (e.g., 'cpu_usage_percent', 'memory_bytes') that help LLMs understand metric semantics and suggest appropriate operations. This enables assistants to recommend unit conversions, detect incompatible metric combinations, and provide context-aware analysis.","intents":["I want the AI to understand that cpu_usage is a percentage and memory is in bytes","Enable the assistant to suggest unit conversions when combining metrics","Help users discover related metrics through semantic tagging"],"best_for":["Teams managing large metric catalogs with diverse units and semantics","Developers building intelligent metric exploration interfaces","Organizations standardizing metric naming and documentation"],"limitations":["Metadata storage requires external system or GreptimeDB extension — not built-in","Semantic understanding depends on metadata quality — garbage in, garbage out","No automatic unit conversion — requires explicit mapping definitions"],"requires":["Metadata storage system (external database or GreptimeDB extension)","Metric naming conventions and documentation","Write permissions for metadata updates"],"input_types":["metric names","metadata definitions (description, unit, tags)"],"output_types":["metric metadata","semantic tags","unit information"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-greptimedb__cap_9","uri":"capability://planning.reasoning.alert.rule.definition.and.anomaly.detection.integration","name":"alert rule definition and anomaly detection integration","description":"Integrates with GreptimeDB's alerting capabilities through MCP operations that allow AI assistants to define alert rules, query anomaly detection results, and recommend alerting thresholds based on historical data. The server exposes operations to create threshold-based alerts, retrieve anomaly detection results, and suggest alert configurations based on statistical analysis of metric distributions. This enables assistants to help users set up monitoring without manual threshold tuning.","intents":["I want the AI to suggest alert thresholds based on historical data patterns","Enable the assistant to create alert rules from natural language descriptions","Help users understand anomalies detected in their time-series data"],"best_for":["Teams setting up monitoring and alerting for time-series data","Developers building AI-assisted observability platforms","Organizations automating alert threshold tuning"],"limitations":["Threshold suggestions are statistical — may not account for business context or SLOs","Anomaly detection accuracy depends on underlying algorithm — no guarantee of catching all anomalies","Alert rule creation requires appropriate permissions — may not be available in all deployments"],"requires":["GreptimeDB with alerting/anomaly detection support","Historical data for statistical analysis","Write permissions for alert rule creation"],"input_types":["metric names","alert thresholds","anomaly detection parameters"],"output_types":["alert rule definitions","anomaly detection results","threshold recommendations"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["GreptimeDB instance running and accessible","MCP client implementation (Claude, other LLM platforms with MCP support)","Network connectivity between MCP server and GreptimeDB","GreptimeDB instance with accessible information_schema","MCP server with schema query permissions","Read-only database credentials","GreptimeDB with authentication enabled","MCP server running with restricted database credentials","TLS/SSL for MCP communication channel","GreptimeDB with time-bucketing functions (DATE_TRUNC, DATE_BIN)"],"failure_modes":["Query translation accuracy depends on LLM's SQL generation capability — complex aggregations may require refinement","No built-in query optimization — relies on GreptimeDB's query planner for performance","Context window limitations may prevent analysis of very large result sets in a single conversation","Schema changes require re-fetching metadata — no built-in change notifications","Large schemas (1000+ tables) may incur latency in metadata retrieval","No schema versioning or historical schema tracking","Parameterization overhead adds ~5-10ms per query","Query timeout enforcement may interrupt long-running analytical queries","Access control granularity limited to table/role level — no row-level security integration","Aggregation accuracy depends on data distribution — sparse data may produce misleading averages","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"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":"2026-06-17T09:51:03.041Z","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=greptimedb","compare_url":"https://unfragile.ai/compare?artifact=greptimedb"}},"signature":"NDzh8PXX4hweAUarFXI2KQJ+XflIyB0ZEiacYYMJl1b030So6rfn9OA2f+hhxNohm5ebGIpF0LXZKQ49HZ35Ag==","signedAt":"2026-06-22T11:54:37.398Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/greptimedb","artifact":"https://unfragile.ai/greptimedb","verify":"https://unfragile.ai/api/v1/verify?slug=greptimedb","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"}}