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Describe dataset

describe_dataset
Read-onlyIdempotent

Columns (id, original name, type, what each means, unit, role, whether it can be summed, range and examples), the team's approved metric definitions with their tie-out checks and glossary, links to other datasets (columns usable as alias.column), data quality (warnings, duplicate rows, empty values), freshness, and the server's current date for resolving relative dates.

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": {
      "dataset_id": "<dataset_id>"
    },
    "name": "describe_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYesDataset id from list_datasets, e.g. ds_ab12cd34ef56

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false. The description adds useful context about returned metadata such as freshness, data quality warnings, and the server's current date, but it does not address permissions, error behavior, or operational limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense noun phrase listing returned information. It is information-rich but poorly structured for scanning, with no lead-in verb and no clear front-loading of the primary purpose.

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?

With no output schema, the description carries the burden of explaining return values and does so thoroughly across columns, metric definitions, links, quality, freshness, and current date. It is largely complete for a one-parameter metadata tool, though usage guidance remains absent.

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 coverage is 100%, and the dataset_id parameter is fully documented with an example and source. The description does not mention dataset_id or add any meaning beyond the schema, so the baseline score of 3 is appropriate.

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 clearly specifies the resource contents: columns, approved metric definitions, dataset links, data quality, freshness, and server date. It does not, however, explicitly differentiate this tool from siblings like list_datasets or query_rows, and it lacks an action verb beyond the title.

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, no mention of alternatives, and no stated prerequisites or exclusions. The agent must infer that this tool is for inspecting dataset metadata before querying or aggregating.

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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