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KOB Live Restaurant Ops Audit

Find a restaurant

find_restaurant
Read-onlyIdempotent

Find a public restaurant listing from a name plus town or postcode, or from a Google Maps URL. Returns candidates. Does not invent a match when Google is not configured.

Input Schema

NameRequiredDescriptionDefault
nameNoRestaurant name
clientNoCalling assistant, used only for the trial link utm_source. Examples: claude, chatgpt, grok, copilot, gemini.
exampleNoWhen true, return the labelled Harbour & Rye fixture and do not call live APIs. Use only if the user asked for an example.
locationNoTown, city, or postcode
googleMapsUrlNoGoogle Maps place URL

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety bar is low. The description still earns credit by adding two behavioral facts not in the annotations: it returns a candidate set rather than a single definitive match, and it explicitly refuses to fabricate a match when Google is unconfigured.

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 short sentences with no filler; the input modes come first and the behavioral caveat about not inventing matches is last. Every sentence carries distinct information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description is expected to cover return behavior; 'Returns candidates' signals a list but does not describe the shape or count of candidates. Given five parameters and no required fields, the description omits how optional inputs combine, though the critical failure-mode behavior is covered.

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 parameter semantics are already documented and a 3 is the baseline. The description adds only a loose mapping to name/location/googleMapsUrl and says nothing about the 'client' or 'example' parameters, leaving the 'plus' phrasing ambiguous about whether name and location are jointly required.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('find a public restaurant listing') and names the two input modes it accepts. It does not explicitly distinguish itself from siblings like get_google_listing or get_yelp_listing, which an agent would have to infer.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when the tool applies by spelling out its input shapes (name + town/postcode, or a Google Maps URL), but it gives no explicit guidance on when to prefer it over get_google_listing or get_yelp_listing, nor any exclusions.

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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