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MCP PostgreSQL Server

by antonorlov
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TypeScript
Remote

Run read-only SQL

query
Read-only

Execute read-only SQL against PostgreSQL and retrieve JSON rows with counts, using parameter placeholders for safe data reads.

Instructions

Run one read-only SQL statement against the connected PostgreSQL database and get rows back as JSON. Send exactly one statement per call (SELECT, WITH, EXPLAIN, or SHOW). It runs inside an engine-enforced read-only transaction, so any write is refused by the database. Use this tool for all data reading, aggregation, and query planning. Returns {rows, rowCount, returnedRows, truncated}, plus hint when truncated is true. Prefer $1, $2 placeholders with the params array over interpolating values. Results are capped at ~32768 bytes; truncated:true means rows were dropped - add LIMIT/WHERE or select fewer columns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesOne SQL statement. Use $1, $2, ... for parameters.
paramsNoPositional parameter values bound to $1, $2, ... placeholders.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.3.0
    • addedInput schema / $schema
      Added value: +"http://json-schema.org/draft-07/schema#"
    • addedInput schema / additionalProperties
      Added value: +false
    • changedInput schema / properties / params / description
      Previous value: -"Query parameters (optional)"New value: +"Positional parameter values bound to $1, $2, ... placeholders."
    • changedInput schema / properties / sql / description
      Previous value: -"SQL SELECT query (use $1, $2, etc. for parameters)"New value: +"One SQL statement. Use $1, $2, ... for parameters."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description adds meaningful behavioral detail: the engine-enforced read-only transaction, the exact return shape {rows, rowCount, returnedRows, truncated}, the ~32768 byte cap with truncated:true behavior, and concrete remediation advice (add LIMIT/WHERE or fewer columns). This goes far beyond what annotations alone provide.

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 compact and every sentence earns its place: purpose, statement constraint, enforcement, usage scope, return shape, binding guidance, and truncation handling. The most important information is front-loaded in the first sentence, and there is no filler or repetition of schema content.

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?

Given there is no output schema, the description fully compensates by detailing the return fields and truncation behavior. It also covers statement type restrictions, read-only enforcement, result size cap, and safe parameter binding. An agent has everything needed to invoke this tool correctly without additional inference.

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?

With 100% schema description coverage, the schema already documents both parameters. The description adds value above the baseline by instructing agents to 'prefer $1, $2 placeholders with the params array over interpolating values,' a safety/security nuance not present in the schema, and by enforcing 'exactly one statement per call.'

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?

The description opens with a precise verb+resource: 'Run one read-only SQL statement against the connected PostgreSQL database and get rows back as JSON.' It further specifies allowed statement types (SELECT, WITH, EXPLAIN, SHOW), which clearly separates it from siblings like list_tables or execute.

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

Usage Guidelines4/5

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: 'Use this tool for all data reading, aggregation, and query planning.' The 'read-only' framing and 'any write is refused' communicate the boundary against writes, though it doesn't explicitly name the write sibling (execute) as the alternative, so it falls just short of full 5.

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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