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MCPg - Production-grade PostgreSQL MCP Server

by devopam
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Python
Hybrid

List constraints

list_constraints
Read-only

Retrieve all constraints (primary key, foreign key, unique, check, exclusion) for a table. Pass schema and table names; set fresh=true to bypass cache and read live after schema changes.

Instructions

List a table's constraints — primary/foreign keys, unique, check, exclusion. Set fresh=true to bypass the cache and re-read live (e.g. after a schema change). Returns a list of objects with name, type, and definition (the constraint SQL).

Example: list_constraints(schema='public', table='orders')

Input Schema

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

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

A4/5.0
Behavior4/5

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

Annotations declare readOnlyHint true, indicating safety. The description adds behavioral details: cache behavior with 'fresh' parameter, return format (list with name, type, definition). This exceeds annotation info.

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 concise: two sentences plus an example. Front-loaded with action, no redundant text. Every sentence adds value.

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 4 parameters and an output schema, the description covers purpose, parameter details (partially), and output format. It lacks explicit usage guidelines but is otherwise complete.

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 only 25% (database has description). The description adds meaning for 'fresh' (bypass cache) and shows usage via example. However, required parameters 'schema' and 'table' lack additional explanation beyond schema.

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 clearly states 'List a table's constraints' and enumerates types (primary/foreign keys, unique, check, exclusion). This distinguishes it from more specific sibling like 'list_foreign_keys'.

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?

While the description explains the 'fresh' parameter usage and provides an example, it does not explicitly state when to use this tool over alternatives or when not to use it. No exclusions or comparisons are given.

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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