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by Mikebenisberchmans
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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describe_table

Retrieve column names, data types, and constraints for a specified table to understand its schema before writing SQL queries.

Instructions

Show column names, types, and constraints for a table.

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

NameRequiredDescriptionDefault
tableYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden, yet it says nothing about read-only nature, required permissions, or behavior for a nonexistent table. The statement of what is returned (columns, types, constraints) overlaps with the output schema, so it adds little behavioral disclosure.

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?

The purpose sentence is short and front-loaded with the key information. The appended 'Args:' block is slightly redundant for a one-parameter tool but costs little.

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?

An output schema exists, so return values need not be explained, and this is a simple one-parameter read operation. Still, nothing addresses error cases, missing tables, or how this differs from list_tables/sample_table, leaving the definition at the minimum viable level.

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%, so the description must compensate, but 'Name of the table to describe' only restates the parameter name. It omits useful detail such as whether a schema qualifier is expected, case sensitivity, or how nested/qualified names are handled.

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 gives a specific verb and resource ('Show column names, types, and constraints for a table'), so an agent immediately knows it returns schema metadata. It does not, however, distinguish itself from siblings like list_tables or sample_table, which also operate on table metadata.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use guidance and no mention of alternatives such as list_tables (for enumeration) or sample_table (for row data). Usage is only implied by the purpose statement, leaving the agent to infer the boundary between this and its siblings.

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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