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MCPg - Production-grade PostgreSQL MCP Server

MCPg - Production-grade PostgreSQL MCP Server

by devopam
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Analyze workload

analyze_workload
Read-only

Identifies the slowest PostgreSQL queries by mean execution time using pg_stat_statements, and reports when the extension is unavailable so you can diagnose workload performance.

Instructions

Return the slowest queries by mean execution time, via the pg_stat_statements extension. Reports availability=false if the extension is not installed.

Example: analyze_workload(limit=10)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "analyze_workload"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
databaseNoOptional: a database id from list_databases, to target a configured secondary (read-only) database; omit for the primary. Database names that are not configured ids are rejected.

Output Schema

NameRequiredDescriptionDefault
availableYes
slow_queriesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.8.4
    • changedInput schema / properties / database / description
      Previous value: -"Optional: target a configured secondary (read-only) database by name; omit for the primary. Call list_databases to see the configured ids."New value: +"Optional: a database id from list_databases, to target a configured secondary (read-only) database; omit for the primary. Database names that are not configured ids are rejected."
  2. First observedv0.6.8

TDQS

A3.7/5.0
Behavior4/5

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

Beyond the readOnlyHint/openWorldHint annotations, the description discloses a real behavioral trait: the tool is unavailable unless the pg_stat_statements extension is installed, and it degrades to availability=false rather than erroring. That failure mode is exactly the kind of context annotations cannot convey; only the return shape is left to the output schema.

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

Conciseness4/5

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

Two tight sentences with the core behavior front-loaded, followed by a short example. The example adds modest value by showing the limit parameter in context, but it borders on redundant with the default already visible in the schema.

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

Completeness4/5

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

For a simple two-parameter read-only tool with an output schema, this covers the essentials: what is measured, the extension dependency, and the degraded response. Only the semantics of limit and the meaning of 'slowest' relative to other ranking options remain unstated.

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

Parameters3/5

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

Schema coverage is 50%: database is well documented in the schema, but limit has no description at all. The example analyze_workload(limit=10) hints at usage and the default but never states that limit truncates the result set to the top N by mean time, so the gap is only partly closed.

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

Purpose4/5

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

The description names a specific verb and resource with its ranking metric: return the slowest queries by mean execution time. That distinguishes it from neighbors like list_active_queries (current activity) and why_is_this_slow, though it never explicitly names an alternative to route away from.

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

Usage Guidelines3/5

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

Usage is implied by the narrow framing (workload = slowest by mean time), and the pg_stat_statements prerequisite is stated. But there is no explicit when-to-use/when-not or pointer to a sibling such as why_is_this_slow or analyze_query_plan for drill-down.

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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