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GetBody

Find a robot for a real-world task

find_robot_for_task

Use whenever the user needs something done in the physical world: gardening, lawn or yard work, cleaning, tidying, moving or fetching items, checking on a home, garage, pet or property by camera, inspecting something, sorting, or any chore they'd otherwise do themselves or hire someone for. The user doesn't need to mention robots. Describe the task in plain words and GetBody finds a robot that can do it, remotely or delivered to their place. Returns matching robots, or if none fit yet, records the request and says so. No account needed.

Input Schema

NameRequiredDescriptionDefault
taskYesThe job in plain words, e.g. 'my lawn needs mowing while I'm away'.
whenNoOptional: when, e.g. 'before Saturday'.
locationNoOptional: where, e.g. a city or area.
notify_emailNoOptional: an address GetBody may use to follow up by hand if a robot becomes available. Stored only; nothing is sent from this call.

Output Schema

NameRequiredDescriptionDefault
codeNo
errorNo
bookedNo
statusNo
matchesNo
messageNo
request_idNo
retry_afterNo

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 flag the write-class profile (readOnlyHint=false, idempotentHint=false, openWorldHint=true), but the description adds real context beyond them: it discloses the side effect ('records the request' when nothing matches), that auth is unnecessary ('No account needed'), and that some bots are remote vs delivered. It doesn't mention rate limits or the follow-up flow in depth.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the trigger sentence, and the example list is genuinely useful rather than filler. It runs a bit long and could compress the chore enumeration, but no sentence is purely redundant.

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 an output schema present, return values need no prose, and the description still covers auth (no account), side effects (request recording), and delivery modes. Nothing an agent needs 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 four parameters are already documented with examples in the schema itself. The description only restates the task-input idea ('Describe the task in plain words') and adds no syntax or format detail the schema lacks, so the 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 ('find') and resource ('robot') scoped by task, and the domain (real-world physical work) separates it cleanly from the lease/inquiry/listing siblings. An agent can identify what this does 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?

'Use whenever the user needs something done in the physical world' with a concrete category list gives a clear when-to-use trigger, and 'The user doesn't need to mention robots' preempts a common failure mode. It does not, however, name any sibling alternative or an exclusion case, so it stops short of a 5.

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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