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mcp-forge

mcp-forge

by carloseduardoscsouza-dot
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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describe_table

Retrieves the schema and column definitions for a specified table, so you can understand its structure before querying or modifying data.

Instructions

Describes the schema and column definitions of 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

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?

No annotations are provided, so the description carries the full disclosure burden; it does imply a safe read operation and names the returned content (schema and column definitions). However, it says nothing about error behavior for nonexistent tables, permissions, or output shape, which is a gap given zero annotation coverage and no 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.

Conciseness5/5

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

One front-loaded sentence with no filler; the resource and the returned content are both stated immediately.

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 one-parameter read-only introspection tool with no output schema, the description conveys enough to call it correctly and roughly what to expect back. It could be slightly stronger by clarifying whether fully qualified table names are accepted and what happens on unknown tables.

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?

There is a single required parameter with 100% schema description coverage, so the schema already documents table_name fully. The description adds no format, qualification, or lookup guidance beyond what the schema provides, making 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 ("Describes") and resource ("a specific table") and clarifies the return content as schema and column definitions. It does not explicitly differentiate itself from siblings like list_tables or execute_query, so it falls just short of a 5.

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 only implied: an agent can infer this is the tool for inspecting a table's structure, but the description never says when to prefer it over list_tables or execute_query. No prerequisites, exclusions, or alternative routing 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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