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compass-food

Compass DaaS

Official

Search restaurants

compass_search
Read-onlyIdempotent

Search restaurants using natural-language dietary queries. Returns ranked results with VeganScore, evidence, and confidence estimates, handling insufficient evidence conservatively.

Instructions

Search restaurants by natural-language dietary query. Returns ranked results with VeganScore, evidence, and confidence. Conservative: returns "unknown" when evidence is insufficient and does not return certification/free-from facts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query, e.g. 'strict vegan ramen in Brooklyn under $20'
user_profileNoOptional dietary profile for per-result fit guidance
locationNoOptional coordinate radius filter for location-aware search
limitNoMaximum number of restaurants to return
include_evidenceNoWhether to include evidence snippets when available
modeNoResponse detail mode; rich includes fuller evidence and reasoningrich

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
compass_request_idNoCompass request identifier for support and tracing
resultsYesRanked Compass restaurant results
query_interpretationNoBest-effort interpretation of the submitted search query
Behavior4/5

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

Beyond readOnlyHint and idempotentHint from annotations, the description adds key behavioral details: conservative handling by returning 'unknown' when evidence is insufficient and not returning certification/free-from facts.

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?

Two sentences, front-loaded with action and result, no wasted words. Every sentence earns its place.

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 a fully described schema and output schema (assumed), the description covers key behavior and results. Minor gap: how to use results with siblings.

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%, so baseline is 3. The description only adds context to the query parameter via 'natural-language dietary query'; no additional meaning for other parameters.

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 clearly states it searches restaurants by natural-language dietary query, returning ranked results with VeganScore, evidence, and confidence. It distinguishes from siblings (decide_fit, enrich) through its search focus.

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 does not explicitly state when to use this tool versus siblings. It implies use for dietary queries but lacks when-not or 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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