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Read-onlyIdempotent

Maps free-text input to valid Overture category slugs, including synonym fallback, for use in place-search and area-summary category filters. Returns ranked taxonomy paths with confidence scores.

Instructions

Free text -> valid Overture category slugs, for the category param the place-search and area-summary tools take.

Lookup only — no geo filtering, no upstream dataset dependency; matches
against a bundled snapshot of Overture's places taxonomy (pinned to
schema v1.9.0). Ranks exact slug match > slug prefix > slug substring >
a match on any taxonomy path segment, so close siblings like "cafe" vs
"coffee_shop" both surface rather than one silently winning. If the
whole query matches nothing, falls back to a lexical phrase-intent
match against a curated synonym lexicon (e.g. "fix my cracked phone
screen" -> mobile_phone_repair). Returns {"results": [{"slug", "path",
"confidence"}, ...]} — path is the root-to-leaf taxonomy (e.g.
["eat_and_drink", "cafe", "coffee_shop"]), confidence is 0-1 and
descending, budgeted like every other tool. An empty/whitespace query
returns {"results": []}. limit is clamped to 0-50, matching every
other tool's limit handling (out-of-range values are not an error).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Install Server

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses the ranking priority (exact slug match > slug prefix > slug substring > path segment), synonym-lexicon fallback, output shape (`{"results": [{"slug", "path", "confidence"}, ...]}`), confidence ordering, empty-query behavior, and limit clamping to 0-50. There is no contradiction with the annotations.

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?

Every sentence contributes a distinct operational fact: purpose, data-source snapshot, ranking order, fallback, output format, empty-query behavior, and limit semantics. The description is detailed but tightly organized and front-loaded with the main purpose before diving into specifics.

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 description covers input semantics, output format, edge cases (empty/whitespace query, out-of-range limit), and behavioral guarantees (confidence descending, budgeted like other tools). Even with an output schema present, the description adds the semantic context needed for correct invocation, and nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides no property descriptions (0% coverage), so the description carries the full burden. It explains that `query` is free text mapped to category slugs with a defined ranking and fallback, and that `limit` is clamped to 0-50 with out-of-range values not treated as errors. The schema only supplies type and default, so the description adds essential meaning.

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 opens with 'Free text -> valid Overture category slugs, for the `category` param the place-search and area-summary tools take,' which states a specific conversion purpose and names the consumer tools. It further distinguishes itself by declaring 'Lookup only — no geo filtering, no upstream dataset dependency,' separating it from sibling geo/search tools.

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

Usage Guidelines5/5

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

It explicitly indicates when to use the tool: to produce the `category` parameter for place-search and area-summary tools. It also gives exclusion cues ('no geo filtering, no upstream dataset dependency') and describes fallback behavior, so an agent can decide between this and sibling tools without additional inference.

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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