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TypeScript
Hybrid

Run query

run_query
Read-only

Execute read-only SQL SELECT queries against Postgres to retrieve data as JSON, with results capped at 1000 rows and schema-qualified table names required.

Instructions

Run a read-only SQL SELECT against Postgres. Only SELECT is permitted — writes, DDL, and multi-statement queries are rejected. Results capped at 1000 rows and returned as JSON ({ columns, rowCount, rows }). Prefer GROUP BY / aggregation over raw dumps. Always qualify table names with schema, e.g. SELECT * FROM analytics.orders. Discover schemas/tables via list_schemas, list_tables, and describe_table first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesRead-only SELECT statement to execute

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Even with readOnlyHint=true, the description adds substantial behavioral detail: enforced SELECT-only policy, rejection of DDL and multi-statement queries, 1000-row cap, and JSON result shape with columns, rowCount, and rows. This exceeds what annotations alone convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized, front-loaded with the core purpose, and every sentence provides useful guidance: restrictions, output format, query style, and discovery workflow.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description fully covers input expectations, constraints, output shape, and how to discover context before querying. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single sql parameter, establishing a baseline of 3. The description adds meaningful guidance beyond the schema by giving an example of schema-qualified queries and explaining result limits and aggregation preferences.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Run a read-only SQL SELECT against Postgres.' This clearly distinguishes the tool from metadata-only siblings like list_schemas, list_tables, and describe_table.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool (read-only SELECT) and what is rejected (writes, DDL, multi-statement queries). It also advises qualifying table names with schema and discovering schemas/tables via sibling tools first, leaving no ambiguity about usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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