SafeDataBaseMCP

describe_table

Get a table's columns, data types, constraints, and foreign keys to understand its schema in a read-only query.

Instructions

Show the columns, types, constraints and foreign keys of one table.

Read-only. Runs immediately, no confirmation needed.

Args: table: Name of the table to describe.

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": "<table>"
    },
    "name": "describe_table"
  }
}

Input Schema

Name	Required	Description	Default
`table`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 5, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states 'Read-only' and 'no confirmation needed,' which explains side-effect and execution expectations. It does not cover error behavior or permission requirements, but those are minor for a simple describe operation.

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 tightly constructed: purpose in the first sentence, behavior in the second, and a minimal Args block. There is no filler, and the most important information is front-loaded. Every sentence earns its place.

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 a single required parameter and an output schema, the description covers the key aspects: what the tool returns, how to specify the table, and that it is read-only and immediate. It doesn't explicitly explain when to prefer it over but siblings, but that gap is already captured under usage guidelines. Overall it is sufficient for invocation.

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?

Schema description coverage is 0%, so the description must compensate. It does: the Args block defines 'table' as 'Name of the table to describe,' giving meaning beyond the bare schema property. It doesn't add constraints like case-sensitivity or schema qualification, but it fully covers the only parameter's semantic role.

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 opens with a specific verb 'Show' and names the exact resource: 'columns, types, constraints and foreign keys of one table.' This clearly distinguishes it from siblings like list_tables (probably lists tables) and propose_change/confirm_change (change tools). The purpose is immediately unambiguous.

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 signals usage context through 'Read-only. Runs immediately, no confirmation needed,' implying this is a safe inspection tool that does not require the change workflow. However, it never explicitly names alternatives or states when not to use it, so the routing relies on inference rather than explicit guidance.

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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