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PgSafe MCP

PgSafe MCP

by johannschiffer
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

describe_table

Retrieve column definitions, data types, nullability, and keys for a specified table to support schema inspection before read-only SQL queries.

Instructions

Retrieve column definitions, data types, nullability, and keys for a specific table.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "table_name": "<table_name>"
    },
    "name": "describe_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
table_nameYesName of the table to inspect

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. 'Retrieve' signals a read-only, non-destructive operation, and it usefully enumerates what metadata is returned (types, nullability, keys). However, it says nothing about behavior on a missing table, permissions required, or formatting of the results.

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?

A single sentence that front-loads the verb and lists the returned metadata with zero filler. Nothing to trim.

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 one-parameter read tool with no output schema, describing the returned fields inline largely satisfies the agent's needs. Missing only edge-case behavior (invalid/nonexistent table, permission errors).

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 100% with one well-documented parameter, so the schema already explains table_name. The phrase 'for a specific table' adds no meaning beyond the schema, making the baseline 3 appropriate.

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?

States a specific verb (Retrieve) and resource (column definitions, data types, nullability, keys) for a named table, which is clearly distinguishable from list_tables and analyze_query. It stops short of explicitly contrasting with those siblings, so it lands just below the top tier.

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: an agent can infer this is the tool for inspecting a single table's schema, but the description never states when to prefer it over list_tables or analyze_query, nor any preconditions.

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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