DataSocial

Describe a table

describe_table

Read-only

One table's columns with types and meanings, its sort key and coverage. Read it before writing SQL on a table.

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

Name	Required	Description	Default
`table`	Yes	The table, e.g. "creators" or "tiktok.creators"

Output Schema

Name	Required	Description	Default
`table`	Yes
`description`	Yes	Size, grain, sort key, coverage and every column with its type and meaning

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 29, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations only declare readOnlyHint=true; the description adds useful behavioral context by disclosing what is returned (typed columns, meanings, sort key, coverage). It says nothing about metadata freshness, permissions, or truncation, but is solid beyond the annotation baseline.

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 tight sentences with no filler, and the return-value summary is front-loaded ahead of the usage directive. Every clause earns its place.

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?

For a single-parameter, read-only metadata tool with an output schema, the description covers purpose, trigger, and returned fields. It is nearly complete, missing only edge-case handling such as unknown tables or namespacing conventions.

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 the single `table` parameter is fully documented in-schema with a qualified-name example. The description contributes no additional parameter meaning, so the baseline of 3 applies.

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 resource (one table) and enumerates what it returns: columns with types and meanings, sort key, and coverage. This distinguishes it functionally from run_sql (metadata vs data), but it never names a sibling explicitly, so it falls short of a 5.

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?

"Read it before writing SQL on a table" gives a clear situational trigger that ties this tool to the run_sql workflow. It provides no explicit exclusions or comparison against list_tables, so it stops short of full when/when-not guidance.

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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