Piping Toolset Engineering Data

Describe a code dataset

describe_dataset

Read-onlyIdempotent

Describe one dataset: the key fields a lookup needs, the value columns with units and which need a temperature, the citation (standard, edition, table), whether lookups return a value or a pointer, the tier at which values are returned, and the daily lookup limits. Returns structure only, no table values.

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

Name	Required	Description	Default
`dataset_id`	Yes	Dataset id from find_dataset

Output Schema

Name	Required	Description	Default
`key`	Yes
`caps`	Yes
`mode`	Yes
`title`	Yes
`columns`	Yes
`citation`	Yes
`verify_at`	Yes
`dataset_id`	Yes
`tier_required`	Yes
`default_column`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 12, 2026

TDQS

A4.5/5.0

Behavior5/5

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

The annotations already establish read-only, idempotent, non-destructive, closed-world behavior. The description adds substantive context beyond those annotations: the exact metadata returned, the value-versus-pointer distinction, tier and daily lookup limits, and the important exclusion of table values.

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: the first front-loads the operation and enumerates returned metadata, while the second states the critical no-values boundary. The list is dense but every clause carries relevant scope information.

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?

For a read-only metadata tool with an output schema and annotations, the description covers purpose, returned structure, and the critical exclusion of values. Missing explicit routing to find_dataset is handled by the input schema.

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 dataset_id parameter is fully documented there, including that it comes from find_dataset. The description adds no further constraints, syntax, or selection guidance for the parameter.

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?

The description opens with a specific verb-resource pair, 'Describe one dataset,' and enumerates the metadata categories returned, such as key fields, value columns, citation, pointer behavior, tier, and limits. This makes its role distinct from value lookup or dataset discovery siblings.

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?

It clarifies that the tool is for dataset structure only and explicitly says no table values are returned, which gives a clear usage boundary against value-retrieval tools. It stops short of naming alternatives such as find_dataset or lookup_value, relying instead on the schema's note that dataset_id comes from find_dataset.

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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