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mcp-database-server

mcp-database-server

by uidoyen
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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describe_table

Retrieves the columns and data types of a specified SQLite table so you can inspect its schema before writing queries.

Instructions

Show the columns of one table, with their types.

Args: table: Table name, exactly as list_tables returned it

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 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 supplied, so the description carries the burden; 'Show' implies a read-only operation and it discloses the return content (columns plus types). It does not cover behavior for nonexistent tables, whether views are included, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

Front-loaded with the core purpose in one sentence, with the parameter note following. The 'Args:' block is slightly formal but short and adds value rather than padding.

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?

An output schema exists, so return values need no explanation, and the tool is simple with one required parameter. Remaining gaps are edge cases (missing table, views) rather than anything blocking correct 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 coverage is 0%, yet the description explains the single parameter's required form: the table name exactly as returned by list_tables, which adds real meaning beyond a bare string type. It does not address qualification or casing rules.

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 and resource: show the columns of one table with their types. This is readily separable from list_tables (which enumerates tables) and run_query, but the description never explicitly contrasts them.

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?

Implies a workflow by requiring the table name 'exactly as list_tables returned it', which steers the agent to call list_tables first. It gives no explicit guidance on when to prefer this over run_query for schema inspection.

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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