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Data Grandparissud

by io.github.pipeworx-io
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Dataset Info

dataset_info
Read-onlyIdempotent

Get metadata for one Grand Paris Sud Open Data dataset (fields/schema, themes, record count) — call before query to learn the column names.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

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

Input Schema

NameRequiredDescriptionDefault
dataset_idYesDataset id from search_datasets.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly=true, idempotent=true, openWorld=true, destructive=false, so safety is covered. The description adds value by disclosing the shape of the return payload (schema, themes, record count), which the annotations do not.

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?

One sentence, front-loaded with the verb and resource, with the workflow hint appended. No wasted words.

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 tool with strong annotations and a declared payload, the definition is sufficient; the absence of an output schema is partly mitigated by naming the returned fields. Only minor edge cases (invalid id behavior) are unaddressed.

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?

With 100% schema description coverage, the single parameter is already documented as coming from search_datasets. The description reinforces this provenance but adds no format or syntax detail beyond the schema, so 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 (Get) and resource (metadata for one Grand Paris Sud Open Data dataset) and enumerates what the metadata contains (fields/schema, themes, record count). It is clearly distinguishable from search_datasets (which supplies the id) and the query tool.

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 directs the agent to call this before query to learn the column names, which is actionable sequencing guidance. It does not state exclusions or alternatives, but the workflow position is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Data Grandparissud toolsInspect
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