mcp-clickvisual
Related Servers
Alternatives to mcp-clickvisual
No user-submitted related servers found.
Related Servers
- AlicenseAqualityDmaintenanceEnables AI assistants to query and manage ClickHouse databases, supporting SELECT queries, DDL/DML statements, and metadata listing.55 npmMIT
- AlicenseAqualityCmaintenanceA read-only MCP server for ClickHouse that enables AI agents to discover schema, sample data, and run read-only SQL queries to investigate observability data like logs, metrics, and traces.66 npmMIT
- FlicenseNot gradedqualityBmaintenanceEnables AI coding agents and other MCP hosts to read Elastic Cloud logs across multiple regions using six read-only tools for overviews, deduplicated error patterns, compact searches, context lookup, full log retrieval, and bulk JSONL export.-
- AlicenseNot gradedqualityBmaintenanceEnables MCP-compatible AI clients to search and retrieve Elasticsearch application logs through a provided search tool.Apache 2.0
- AlicenseNot gradedqualityDmaintenanceEnables executing SQL queries, listing databases, and listing tables on a ClickHouse cluster through natural language.Apache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables interaction with ClickHouse databases via MCP, providing tools to list databases and tables and execute safe SELECT, SHOW, and DESCRIBE queries.34 npmMIT
TDQS
Scored across 7 tools
Each tool maps to a distinct action/resource: discovery hierarchy (instances/databases/tables), table ID resolution, log querying, histogram aggregation, and auth check. search_logs and get_log_charts both use table_id but clearly differ by returning raw logs versus count distribution.
All names follow a consistent verb_noun snake_case pattern (list_*, search_logs, resolve_table_id, get_log_charts, check_auth). There are no mixed conventions or vague verbs.
Seven tools is well-scoped for a read-only log exploration server. The discovery, search, histogram, and auth pieces all serve the same purpose without bloat.
The set covers the core workflow end-to-end: authenticate, discover instances/databases/tables, resolve the required numeric table_id, then search raw logs or view histograms. The only notable gap is schema/field discovery, which agents may need to construct meaningful search_logs queries.