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describe_table

Inspect a table's full schema: column names, types, nullability, comments, keys, indexes, and estimated row count before writing SQL.

Instructions

查看一张表的完整结构：所有列的名字、类型、是否可空、以及列注释；外加主键、外键、索引和估算行数。写 SQL 之前必须先调这个确认列名。

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

Name	Required	Description	Default
`table`	Yes	表名，大小写敏感，例如 final_HH_CSES
`schema`	Yes	schema 名，例如 cses_data

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 11, 2026

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. The read verb '查看' implies a safe read, and the enumerated return contents (PK, FK, indexes, row counts) tell the agent what to expect. However, it says nothing about whether the data is live or from the metadata index (siblings rebuild_metadata_index / metadata_index_status hint at caching), nor about permissions or latency.

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?

A single dense sentence whose output list is front-loaded, followed by the one imperative usage rule. Nothing is padded or redundant.

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?

An output schema exists, so return values need no explanation, and the description still summarizes them. Both required parameters are covered by the schema, and the invocation precondition is stated, leaving nothing critical missing for a read-only introspection tool.

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%, and both parameters (schema, table) are already documented in the schema, including casing sensitivity and examples. The description adds no additional meaning beyond that, so the baseline 3 applies.

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 (查看/view) and resource (一张表/a table), then enumerates exactly what is returned: column names, types, nullability, comments, plus primary keys, foreign keys, indexes, and estimated row count. An agent can distinguish it from siblings like list_tables or sample_rows without opening the 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?

Explicitly mandates when to call it: '写 SQL 之前必须先调这个确认列名' (must call this before writing SQL to confirm column names). That is a clear precondition, but it names no alternative tools (e.g., find_variables, search_metadata) or conditions for choosing among them.

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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