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by rohanbeingsocial
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

describe_data

Lists readable tables, columns, estimated row counts, and named data sources to inspect available data before writing SQL or selecting a source.

Instructions

List what you can read: the database tables and columns query_data can use (with estimated row counts), and the app's named data sources for use_source. Call it before writing SQL against tables you have not seen.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "describe_data"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose the payload: tables, columns, estimated row counts, and named data sources, including the "estimated" qualifier that warns against relying on counts as exact. It stops short of saying whether the listing is live/cached or how large a result can be, which matters for a zero-parameter exhaustive listing.

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, no filler, and the content of the listing is front-loaded before the usage instruction. Every clause earns its place by naming either a return field or a consumer tool.

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?

No output schema exists, yet the description enumerates the return contents well enough for an agent to know what it will get and to route it to query_data or use_source. Minor gap: no hint about result size, freshness, or what an empty catalog means.

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?

The tool takes no parameters, so there is nothing to disambiguate and the baseline of 4 applies. The description correctly implies a single no-argument call returning the full catalog.

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 concrete verb and the exact resources enumerated: database tables/columns usable by query_data, plus the app's named data sources for use_source. It distinguishes itself from the two sibling tools that consume its output, so an agent can tell what it returns versus what query_data and use_source do.

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

Usage Guidelines5/5

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

"Call it before writing SQL against tables you have not seen" is an explicit when-to-use rule with a clear precondition (unfamiliar tables). Naming query_data and use_source as the downstream consumers makes the alternative-selection implicit but unambiguous.

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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