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MCPg - Production-grade PostgreSQL MCP Server

Run analytical query

run_analytical_query
Read-only

Execute long-running read-only analytical queries on PostgreSQL using a dedicated connection pool with elevated timeout and memory. Returns columns, rows, row count, and truncation flag.

Instructions

Run a read-only SELECT that may take longer than the standard limit — for genuine analytical work (large aggregations, multi-table joins, window functions, DISTINCT/GROUP BY over millions of rows). Validated by the same allowlist as run_select, but executed on a DEDICATED connection pool (isolated from the fast-path tools) with an elevated, bounded timeout. Prefer run_select for ordinary queries; reach for this only when a query legitimately needs more time. timeout_ms overrides the per-call budget (clamped to the server's configured maximum, MCPG_ANALYTICAL_MAX_TIMEOUT_MS); work_mem (e.g. '256MB') elevates sort/hash memory for this statement. Runs against the primary database. Returns an object with columns, rows, row_count, and truncated (true when more rows than max_rows were produced) — same shape as run_select.

Example: run_analytical_query(sql='SELECT c, count(*) FROM big GROUP BY c', timeout_ms=180000)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYes
max_rowsNo
work_memNo
timeout_msNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
columnsYes
row_countYes
truncatedYes
Behavior4/5

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

Adds context beyond annotations: dedicated connection pool, elevated timeout, primary database. Annotations already mark read-only.

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?

Concise, front-loaded purpose, efficient sentences covering all key aspects without redundancy.

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?

Covers behavior, connection isolation, timeout behavior, return shape (known from output schema), and distinguishes from siblings.

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?

Describes timeout_ms clamping and work_mem usage. Example clarifies usage. Compensates for 0% schema coverage.

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?

Clearly states it runs read-only analytical SELECT queries requiring longer time, distinguishing from standard queries.

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?

Explicitly advises to prefer run_select for ordinary queries and use this only when more time is needed.

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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