The OtterStax MCP Server
OfficialRelated Servers
Alternatives to The OtterStax MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to query MySQL, ClickHouse, Trino, and S3-compatible object stores through the same local daemon used by VSCode, including running SQL from files and browsing buckets.1MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to query and explore PostgreSQL and MySQL clusters from a single main configuration, with dynamic per-database routing, automatic database discovery and sync, table/schema inspection, and hierarchical write-rule protection that can block or allow specific SQL mutation operations.139 npmMIT
- FlicenseNot gradedqualityDmaintenanceEnables AI agents to execute SQL queries and introspect PostgreSQL schemas, tables, and indexes with read-only safety by default. Supports optional write operations and works with Claude, LangChain, and other agents via stdio or HTTP transports.-
- AlicenseAqualityCmaintenanceLets AI agents and developers inspect and query local MySQL, Redis, and MongoDB instances over STDIO or authenticated loopback HTTP, with read-only defaults and allowlisted non-production writes that require explicit opt-in. Production connections always reject writes, and database-enforced read-only accounts are recommended.18Apache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables MCP clients to explore and query Postgres, MySQL, and BigQuery databases—listing schemas and tables, describing columns, sampling rows, running read-only SQL, and, when permitted, executing writes—with schema allowlists, hidden columns, response budgets, and audit logging. Runs on Node/Docker over stdio or Streamable HTTP, or as a Cloudflare Worker with OAuth and Hyperdrive.101 npmMIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to query Postgres, BigQuery and DuckDB read-only through policy-checked SQL, with AST-based guards that refuse writes and dangerous functions, column masking on the way out, and an audit log of every query. Also exposes schema browsing and query-planning tools so agents can see allowed tables, columns and costs before running anything.MIT
TDQS
Scored across 8 tools
Each tool has a fairly clear role: discovery (list_databases/list_schemas/list_tables/describe_table), execution (execute_sql), planning (explain_query), and profiling (table_stats). However, execute_sql can already run any statement including EXPLAIN, so explain_query overlaps, and search_objects versus list_tables/describe_table have partially overlapping lookup purposes.
Seven of eight tools follow a clean verb_noun pattern (explain_query, execute_sql, list_databases, list_schemas, list_tables, describe_table, search_objects). table_stats deviates to a noun_noun form, a minor inconsistency but still readable.
Eight tools is well-scoped for a SQL database engine, covering discovery, execution, planning, and profiling without redundancy. Each tool earns its place.
The surface covers exploration (databases, schemas, tables, columns, search) plus execution, explain, and profiling, which is strong for a query engine. Minor gaps like query cancellation or session/connection management are absent but not blocking for typical agent workflows.