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by hostedinthebarn
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

describe_table

Read-only

Reveals a table's columns, data types, and keys so you can inspect its structure before writing read-only SQL queries.

Instructions

Show a table's columns, types and keys.

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

NameRequiredDescriptionDefault
tableYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already state readOnlyHint=true and openWorldHint=false, so the safety profile is clear. The description adds that the result includes columns, types and keys, but since an output schema exists, this does not add substantial behavioral context beyond structured fields. It does not contradict the annotations.

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 a single, front-loaded sentence with no filler. Every word contributes to stating what the tool returns.

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-only introspection tool with annotations and an output schema, the description is mostly complete. The main gap is the lack of guidance on the "table" argument's expected format and no differentiation from list_tables or run_query.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for the single required "table" parameter. The description only implies that "table" identifies the target table; it does not specify naming format, qualification requirements, or error behavior for nonexistent tables. The schema carries almost no semantic detail, and the description does not compensate.

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 uses a specific verb ("Show") and resource ("a table's columns, types and keys"), making the operation clear. It does not explicitly distinguish itself from siblings like list_tables or run_query, so it falls 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 implied by the phrase "Show a table's columns, types and keys"—an agent can infer this is for schema introspection. However, there is no explicit when-to-use guidance, no mention of alternatives such as list_tables or run_query, and no stated prerequisites.

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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