Piping Toolset Engineering Data

Look up one cited code-table value

lookup_value

Read-onlyIdempotent

Return one value from one row and one column of a code-table dataset, with its citation (standard, edition, table, row, column), unit and the tabulated entries used. Temperature columns take T in °F; allowable-stress and flange-rating columns interpolate linearly between the two bracketing tabulated temperatures and never extrapolate. Datasets in pointer mode return the citation with value null. The key must identify exactly one row; use list_keys for identifiers.

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>",
      "key": "<key>"
    },
    "name": "lookup_value"
  }
}

Input Schema

Name	Required	Description
`key`	Yes	Key fields from describe_dataset, e.g. {"nps": 2} or {"spec": "A106", "grade": "B", "T": 650}
`column`	No	Value column; optional when the dataset has a default column
`dataset_id`	Yes	Dataset id from find_dataset

Output Schema

Name	Required	Description	Default
`mode`	Yes
`text`	Yes
`unit`	Yes
`value`	Yes
`column`	Yes
`notice`	Yes
`citation`	Yes
`rows_used`	Yes
`verify_at`	Yes
`provenance`	Yes
`tier_required`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 12, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the read-only and idempotent annotations, the description discloses important behavior: interpolation occurs only between bracketing temperatures, extrapolation never occurs, temperature keys use °F, and pointer-mode datasets return a citation with a null value. These details materially affect how an agent interprets and trusts the result.

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?

The description is dense and front-loaded: the first sentence defines the return value and citation, later sentences add interpolation, pointer-mode, and key constraints. Every sentence contributes operationally important information without repetition.

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?

The tool's safety profile is supplied by annotations, its input shape is fully described by the schema, and its output is covered by an output schema. The description completes the picture by explaining interpolation, non-extrapolation, pointer-mode behavior, and key uniqueness.

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?

Schema coverage is 100%, so the schema already documents dataset_id, column, and key fields. The description still adds useful semantics by stating that the key must identify exactly one row, that temperature values are in °F, and that interpolation rules apply to specific column types.

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 states a specific verb and resource: return one value from one row and one column of a code-table dataset, along with its citation. It clearly distinguishes the operation from list_keys by directing identifier lookup there, so an agent can identify the tool without opening the schema.

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 gives a clear usage condition that the key must identify exactly one row and explicitly points to list_keys for identifiers. However, it does not explicitly say when to use find_dataset or describe_dataset first, leaving part of the workflow to schema descriptions and sibling context.

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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