GreptimeDB
MCP ServerFree** - Provides AI assistants with a secure and structured way to explore and analyze data in [GreptimeDB](https://github.com/GreptimeTeam/greptimedb).
Capabilities10 decomposed
time-series data querying via natural language
Medium confidenceEnables AI assistants to translate natural language queries into GreptimeDB SQL statements for time-series data exploration. The MCP server acts as an intermediary that parses user intent, constructs parameterized SQL queries, and returns structured result sets with schema awareness. This allows non-SQL-fluent users to explore metrics, logs, and time-series data through conversational interfaces without writing raw SQL.
Implements MCP protocol as a standardized bridge between LLM assistants and GreptimeDB, enabling schema-aware query generation with built-in safety constraints and result streaming rather than generic database connectors
Provides tighter LLM-database integration than generic SQL tools because it understands GreptimeDB's time-series semantics (retention policies, downsampling, time bucketing) natively
schema introspection and table discovery
Medium confidenceProvides 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.
Caches and exposes GreptimeDB's time-series specific schema properties (retention policies, compression settings, time column definitions) alongside standard relational metadata, enabling context-aware recommendations
More comprehensive than generic database introspection because it surfaces time-series specific attributes that affect query strategy (e.g., downsampling rules, TTL policies)
secure parameterized query execution with access control
Medium confidenceExecutes 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.
Implements MCP-level query validation and parameterization before GreptimeDB execution, with configurable timeout and result-set limits, preventing both malicious and accidental resource exhaustion from LLM-generated queries
Provides stronger isolation than direct database connections because the MCP server acts as a security boundary with query inspection and rate limiting, not just credential abstraction
time-series data aggregation and downsampling
Medium confidenceEnables 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.
Abstracts GreptimeDB's native time-bucketing and aggregation functions through semantic MCP operations, allowing LLMs to request 'hourly averages' without understanding SQL window functions or GreptimeDB-specific syntax
More efficient than post-query aggregation in the LLM layer because it leverages GreptimeDB's optimized time-series aggregation engine, reducing data transfer and computation
multi-table join and correlation analysis
Medium confidenceAllows 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.
Provides semantic join operations that understand time-series alignment requirements, automatically handling timestamp matching and window boundaries rather than exposing raw SQL JOIN syntax to LLMs
Reduces join complexity for LLMs compared to raw SQL because it abstracts time-window alignment and prevents common temporal join errors like mismatched granularities
query result streaming and pagination
Medium confidenceStreams 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.
Implements cursor-based pagination at the MCP protocol level with streaming support, allowing LLMs to consume large result sets incrementally without materializing entire datasets in memory
More memory-efficient than batch result fetching because it streams results in configurable chunks and maintains cursor state, preventing context window exhaustion
query performance analysis and optimization suggestions
Medium confidenceAnalyzes 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.
Translates GreptimeDB EXPLAIN PLAN output into LLM-consumable optimization suggestions, bridging the gap between low-level query metrics and high-level performance recommendations
More actionable than raw EXPLAIN output because it synthesizes execution plans into natural language recommendations that LLMs can understand and communicate to users
data retention and ttl policy enforcement
Medium confidenceExposes 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.
Integrates GreptimeDB's table-level TTL and retention policies into MCP operations, enabling LLMs to make retention-aware query recommendations and alert users about data availability
Provides better user experience than silent data deletion because assistants can proactively warn about retention windows and suggest appropriate time ranges
metric metadata and semantic tagging
Medium confidenceManages 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.
Provides semantic metadata layer on top of GreptimeDB metrics, enabling LLMs to understand metric units, descriptions, and relationships rather than treating them as opaque column names
Improves LLM reasoning about metrics compared to raw schema because semantic tags and unit information enable unit-aware calculations and incompatibility detection
alert rule definition and anomaly detection integration
Medium confidenceIntegrates 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.
Bridges natural language alert descriptions to GreptimeDB alert rule creation, with statistical threshold recommendations based on historical data distributions rather than manual configuration
More user-friendly than manual alert configuration because it suggests thresholds based on data analysis and translates natural language into alert rules
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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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
- ✓Developers building intelligent query builders
- ✓Teams implementing AI-powered data exploration interfaces
- ✓Organizations with dynamic schemas requiring real-time metadata sync
- ✓Production environments requiring secure AI-database integration
- ✓Multi-tenant systems where data isolation is critical
Known 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
- ⚠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
Requirements
Input / Output
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About
** - Provides AI assistants with a secure and structured way to explore and analyze data in [GreptimeDB](https://github.com/GreptimeTeam/greptimedb).
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