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Piping Toolset Engineering Data

by com.pipingtoolset
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List the row identifiers of a dataset

list_keys
Read-onlyIdempotent

List the key identifiers of a dataset's rows (for example spec, grade, class, size, and the row position line) so lookup_value can address exactly one row. Returns identifiers only, never table values, at most 50 per call; prefix filters on the first key field. For pointer datasets it returns the key schema without rows.

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": "list_keys"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum identifiers to return (default 20, max 50)
prefixNoOnly identifiers whose first key field starts with this text
dataset_idYesDataset id from find_dataset

Output Schema

NameRequiredDescriptionDefault
keysYes
modeYes
truncatedYes
dataset_idYes
key_fieldsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description still adds substantive behavior: it returns identifiers and never table values, caps output at 50 per call, explains prefix semantics on the first key field, and discloses the special case where pointer datasets yield only a key schema.

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?

Three dense sentences, each earning its place, with purpose and the output contract front-loaded before the pagination limit and the pointer-dataset caveat. No filler or redundant restatement of the title.

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 simple read-only listing tool with a full-coverage schema and an output schema, the description covers purpose, output contract, limits, filtering semantics, and the one edge case. Nothing an agent needs in order to call this correctly is missing.

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%, so all three parameters are already documented in the schema. The description restates the prefix filter's meaning ('filters on the first key field') and the per-call cap, adding little beyond what the schema already says, 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 and resource ('List the key identifiers of a dataset's rows') and enumerates concrete examples (spec, grade, class, size, line). It distinguishes itself from the sibling that consumes its output by noting it 'Returns identifiers only, never table values', which is exactly the contrast with lookup_value.

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?

The workflow context is explicit: identifiers are listed 'so lookup_value can address exactly one row', and it warns that for pointer datasets it returns the key schema without rows. It lacks an explicit negative routing statement (e.g. 'use describe_dataset to inspect columns'), so it stops just 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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