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Seemor Restaurant Intelligence

Search Restaurants

search_restaurants
Read-onlyIdempotent

Search for restaurants near a location. Returns graded, ranked results with cuisine, price level, and Seemor analysis summaries. Fully analyzed restaurants (coverage_level 'full', letter grade) come first; when fewer than limit are available, quick-read restaurants are appended after them, marked coverage_level 'basic' with grade null and a preliminary_band (e.g. 'A-range') graded from review analysis, not star ratings. total_in_area = analyzed_in_area + preliminary_in_area; a message explains when quick-read rows are included.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoSort order: "grade" (default, best first) or "distance" (nearest first).
limitNoMaximum results to return. Default 10, max 10.
cuisineNoFilter by cuisine type (e.g. "Italian", "Japanese"). Case-insensitive substring match.
latitudeYesLatitude of the search center (-90 to 90).
longitudeYesLongitude of the search center (-180 to 180).
min_gradeNoMinimum letter grade to include (e.g. "B+"). Grades: A+, A, A-, B+, B, B-, C+, C, C-, D, F.
radius_kmNoSearch radius in kilometers. Default 2, max 10.
price_levelNoFilter by price level: "$", "$$", "$$$", or "$$$$".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNo
messageNo
resultsNo
total_in_areaNo
analyzed_in_areaNo
preliminary_in_areaNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the safety profile is covered. The description adds substantial behavioral context: the two-tier result ordering (full-analyzed first, basic appended), the grading semantics (grade null for basic, preliminary_band from review analysis), and the total_in_area formula. It also discloses the 'message explains when quick-read rows are included'. That's meaningful behavior beyond 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?

The description is a single dense paragraph but every sentence earns its place: it states the purpose, the result ranking, the two-tier coverage model, the meaning of total_in_area, and the messaging behavior. It's front-loaded with the core function and then layers specifics. For a tool with 8 parameters, it's appropriately sized without fluff.

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?

Given the complexity (8 params, output schema exists), the description covers the key behavioral nuances: ordering, coverage levels, how grades are assigned, and the total formula. It doesn't explain return fields in detail, but there's an output schema that likely carries that. It could mention pagination or max values, but those are in the schema. The description is complete enough for an agent to call correctly and interpret results.

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 8 parameters are already documented with types and descriptions. The tool description adds a bit around limit (when fewer than limit are available), and it mentions 'preliminary_band' which relates to grading but doesn't add new parameter semantics. It also implies the default sort is 'grade', which matches the schema's description. Since the schema carries the load, baseline 3 is appropriate; the description doesn't add extra param-level meaning beyond what's in 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?

Description says 'Search for restaurants near a location' (verb+resource+scope), and then details exactly what results it returns: graded, ranked results with cuisine, price, and analysis. It distinguishes itself from sibling tools: it's the search tool, while find_restaurant and lookup_restaurant are probably direct lookups, explore_area is area-focused, recommend is recommendation. The specific mention of the two-tier coverage level is a clear differentiator.

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 says 'when fewer than limit are available, quick-read restaurants are appended' — that's a when/condition for the inclusion of basic rows. It does not explicitly name sibling tools or contrast alternatives, but it gives clear operational guidance on how result sets behave, which helps an agent decide when to use it and how to interpret results. No explicit exclusions or alternates, but the context is clear for a search tool.

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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TDQS

A4.4/5.0
Disambiguation4/5

Most tools are clearly distinct: find_restaurant searches by name, search_restaurants searches by location, explore_area gives area statistics, and recommend gives a final ranked recommendation. Some minor overlap exists between lookup_restaurant and ask_about_restaurant and between search_restaurants and recommend, but the descriptions carefully separate structured details from Q&A and search from final recommendations.

Naming Consistency4/5

Tool names mostly follow a clear verb_noun pattern: find_restaurant, lookup_restaurant, search_restaurants, explore_area, and ask_about_restaurant. The exception is recommend, which is a bare verb with no object, creating a minor inconsistency but not enough to cause confusion.

Tool Count5/5

Six tools is well-scoped for a restaurant intelligence server, covering discovery, lookup, area exploration, Q&A, and recommendations. Each tool earns its place and there is no bloat or redundancy.

Completeness5/5

The tool surface covers the full workflow: find or search restaurants, look up details, ask specific questions, explore area context, and get a final recommendation. There are no obvious dead ends, and the tool descriptions explicitly explain how IDs flow from one tool to another.

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