Whirrwork

Find robot types and models for a job

find_robots_for_job

Read-onlyIdempotent

Which kinds of robot do a job (primary type first, then alternatives), with the commercial models we have catalogued for each type: maker-published key specs, list price where published, and the Whirrwork assessment (a score only when independent trials or tests exist; otherwise "Not enough evidence to rate"). Use search_use_cases first to get the job id. Example: {"job":"crop-spraying-drone"}

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": {
      "job": "<job>"
    },
    "name": "find_robots_for_job"
  }
}

Input Schema

Name	Required	Description	Default
`job`	Yes	Job (use case) id from search_use_cases

Output Schema

Name	Required	Description	Default
`job`	Yes
`note`	No
`types`	Yes
`model_count`	No
`methodology_url`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 27, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already cover the safe read-only, idempotent, non-open-world behavior. The description adds useful behavioral context about result ordering, model attributes returned, and the evidence threshold for Whirrwork assessment scores, including the fallback text when evidence is insufficient.

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 front-loaded and compact: it explains the output, the prerequisite, and the assessment caveat in two sentences, followed by a brief example. Every sentence contributes useful 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?

With rich annotations, 100% schema coverage, one parameter, and an output schema, the description is complete enough for correct invocation. It covers the prerequisite, expected output structure, and the rating evidence caveat without redundant detail.

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 single parameter already has a description plus examples. The description reinforces that the job id comes from search_use_cases and includes an example, but adds little semantic detail beyond the schema.

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 resource and output shape: robot types for a job, primary first then alternatives, plus catalogued commercial models with specs, prices, and assessments. It also names the upstream tool needed to obtain the job id, which distinguishes it from siblings like search_use_cases and get_robot_model.

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 explicitly says to use search_use_cases first to get the job id, giving clear sequencing guidance. It does not state when not to use this tool or compare it directly with alternatives beyond that prerequisite, so it falls short of full when/when-not/alternative 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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