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MCPg - Production-grade PostgreSQL MCP Server

by devopam
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Audit database

audit_database
Read-only

Run a comprehensive PostgreSQL schema audit to identify performance risks, memory pressure, checkpoint inefficiencies, lock contention, and dead tuple bloat. It returns an overall health score, top issues, and actionable recommendations.

Instructions

Run a deep, comprehensive DBA-level database performance, logs, and health audit over the specified schema. Scans memory, checkpoints, temp file spills, contention locks, dead tuple cleanliness, and optionally scans custom logging tables. Set fresh=true to bypass the cache and re-read live (e.g. after a schema change). Returns an object with timestamp, database, version, overall_health ('GOOD' / 'WARNING' / 'CRITICAL'), health_score (int), categories (per-area results), top_issues, recommendations, and raw_stats_snapshot.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
freshNo
schemaYes
databaseNoOptional: target a configured secondary (read-only) database by name; omit for the primary. Call list_databases to see the configured ids.
log_tableNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
versionYes
databaseYes
timestampYes
categoriesYes
top_issuesYes
health_scoreYes
overall_healthYes
recommendationsYes
raw_stats_snapshotYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / fresh
      Added value: +{
+  "default": false,
+  "title": "Fresh",
+  "type": "boolean"
+}
  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?

Annotations declare readOnlyHint=true, and the description adds context by naming the resources scanned (memory, checkpoints, etc.), which are all read operations. It discloses caching behavior with fresh parameter. No contradictions; description adds value beyond annotations.

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?

The description is a single paragraph that is fairly concise and front-loads the purpose. It avoids fluff but could benefit from bullet points for the return object fields or parameters. Still efficient.

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?

Given the presence of an output schema and 4 parameters, the description covers the return structure (fields listed) and key behavioral points. It lacks mention of permission requirements or failure modes, but is generally complete for a read-only audit tool.

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 low (25%). The description adds meaning for fresh (cache bypass) and log_table (custom logging tables), and implies schema scope, but does not fully explain database or schema parameters beyond minimal wording. Compensates partially but not completely.

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 clearly states the tool performs a comprehensive DBA-level audit, listing specific areas it scans and return fields. It distinguishes itself from siblings by being 'deep, comprehensive DBA-level', but could explicitly differentiate from similar tools like check_database_health.

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?

The description provides guidance on when to use fresh=true (after schema change) but lacks explicit when-not-to-use, prerequisites, or alternatives. It implies use for comprehensive audits but offers no exclusions.

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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