mcp-clickhouse-long-running
Related Servers
Alternatives to mcp-clickhouse-long-running
No user-submitted related servers found.
Related Servers
- Apache 2.0
- AlicenseAqualityDmaintenanceA DataOps-focused MCP server for ClickHouse that provides query optimization advice, pipeline latency analysis, and data quality monitoring, with read-only safety.830 PyPIMIT
- AlicenseAqualityAmaintenanceEnables MCP clients to explore ClickHouse schemas, run analytical queries, and manage the database with statement-aware security modes and access-control flags.9248 npm2MIT
- FlicenseAqualityCmaintenanceRead-only MCP server for ClickHouse that allows listing databases and tables, describing schemas, and running SELECT queries.4-
- AlicenseNot gradedqualityCmaintenanceEnables interaction with ClickHouse databases via MCP, providing tools to list databases and tables and execute safe SELECT, SHOW, and DESCRIBE queries.58 npmMIT
- AlicenseAqualityCmaintenanceRead-only MCP server for large-scale cross-sectional quant analysis of US stock market data (1.49 billion rows) using ClickHouse.7MIT
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: list_databases for database enumeration, list_tables for table metadata within a database, and run_select_query for executing queries. There is no overlap in functionality, making tool selection straightforward for an agent.
All tools follow a consistent verb_noun naming pattern (list_databases, list_tables, run_select_query), using snake_case throughout. This predictability aids in understanding and usage without any deviations.
With only 3 tools, the server feels thin for a database interaction scope, lacking operations like data manipulation (INSERT/UPDATE/DELETE), schema modification, or query execution beyond SELECT. While core functions are present, the count is borderline low for comprehensive database management.
The tool set is significantly incomplete for ClickHouse database operations, covering only listing and SELECT queries. Missing are essential CRUD operations (e.g., INSERT, UPDATE, DELETE), schema changes (CREATE/ALTER/DROP), and other query types (e.g., SHOW, DESCRIBE), which will likely cause agent failures in broader workflows.