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database-mcp

by thhart
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Python
Remote

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    • postgres-mcp

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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.8/5.0

    Scored across 18 tools

    Disambiguation5/5

    Each tool targets a distinct concern: cursor-paginated queries (query/fetch/close), schema exploration (tables/describe/overview/search_objects), statistical analysis (profile/count/sample/relations/join_path), explain plans, and connection profile management (profiles/profile_add/profile_remove/profile_test). No overlapping responsibilities that would cause an agent to misselect.

    Naming Consistency4/5

    Tool names are mostly snake_case verbs or nouns that clearly map to actions (query, fetch, describe, count) or objects (tables, profiles). The profile_add/profile_remove/profile_test trio uses a consistent prefix. The only minor inconsistency is mixing singular nouns like 'profile' with plural 'profiles' and standalone verbs, but this does not impair predictability.

    Tool Count4/5

    At 18 tools, the set is slightly heavy but each tool earns its place—covering query execution, pagination, schema discovery, row sampling, join pathing, explain, column statistics, and connection management. It leans toward the upper bound of reasonable scope for a database MCP server, but avoids bloat.

    Completeness5/5

    The tool surface covers the full read-side lifecycle: discover schema (overview, tables, describe, search_objects), analyze data (profile, count, sample, relations, join_path), execute and paginate queries (query, fetch, close), inspect performance (explain), and manage connections (profiles, profile_add/remove/test, status). No critical operations are missing for the intended read/diagnostics purpose.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources