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lu-query

lu-query

by zademy
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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describe_table

Retrieve a table's column names, data types, nullability, defaults, and comments before writing SELECT, INSERT, or UPDATE on unverified tables to avoid guessed column names.

Instructions

Describe the columns of a table: name, data type, nullability, default value and comment. ALWAYS call this before writing a SELECT/INSERT/UPDATE against an unverified table, so column names and types are known rather than invented.

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
tableYesTable name.
schemaNoSchema (owner) of the table. Optional — defaults to the server's current schema; omit rather than guess.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.16.3

TDQS

A4.1/5.0
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 burden. It discloses the operational protocol (verify before DML) and the return shape, but says nothing about permissions required, cost, or whether the call is read-only — the read-only nature is only implied by the verb 'describe'.

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?

Two sentences with zero waste: the first front-loads what the tool returns, the second front-loads the imperative usage rule. Nothing is repeated from the schema.

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

Completeness5/5

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

There is no output schema, but the description enumerates the returned column attributes, so an agent knows what it will get back. Combined with a fully documented 2-parameter schema, nothing needed to call this correctly is missing.

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?

Schema description coverage is 100%, so the schema already documents both parameters — including the useful 'omit rather than guess' note on schema. The description adds no additional parameter meaning, making 3 the correct baseline.

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?

States a specific verb (describe) and resource (a table's columns) and enumerates exactly what is returned: name, data type, nullability, default value and comment. That field-level specificity separates it from get_ddl, list_lob_columns and list_constraints without the agent opening any schema.

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

Usage Guidelines4/5

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

Gives an explicit trigger: call it before writing SELECT/INSERT/UPDATE against an unverified table, with the rationale that column names and types should be known rather than invented. It does not name alternative metadata tools (get_ddl, get_table_stats) or state when not to bother, so it stops short of a 5.

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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