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drill-mcp

drill-mcp

Official
by apache
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

describe_table

Inspect a table's schema by listing its columns, their data types, and whether each column accepts null values. Use this to understand table structure before querying.

Instructions

List a table's columns with their types and nullability.

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

Input Schema

NameRequiredDescriptionDefault
tableYes
schemaYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, but the read-only nature is implicit from the 'describe' action. The description does not disclose potential errors, permission requirements, or side effects, though the output schema covers return values. It adds minimal behavioral context beyond the core function.

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, concise sentence that front-loads the verb and resource. No unnecessary words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and the presence of an output schema, the description provides the core purpose but lacks guidance on parameter usage and any edge cases. It is a minimum-viable description that is not misleading but could be more thorough.

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%, and the description does not explain the parameters schema and table. While the parameter names are self-explanatory, the description adds no additional meaning beyond what the schema already provides (e.g., format, constraints, or which parameter is which).

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 explicitly states the tool's action (List), resource (table's columns), and specific output details (types and nullability). This clearly distinguishes it from sibling tools like list_tables and list_schemas.

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 implies usage for inspecting a specific table's schema but provides no explicit comparison to alternatives like run_query or any conditions. It is adequate but lacks direct guidance on when to choose this over other tools.

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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