Seemor Restaurant Intelligence

Find Restaurant

find_restaurant

Read-onlyIdempotent

Find restaurants by name. Returns matching restaurants with their Seemor IDs, which can be passed to lookup_restaurant for full details. Use the city parameter to disambiguate common names. Each result reports coverage_level: 'full' (letter grade), 'basic' (preliminary read: grade null, preliminary_band, tldr, caveat) or 'none' (no analysis yet).

Input Schema

TableJSON Schema

Name	Required	Description
`city`	No	City to narrow the search. Example: 'London', 'New York', 'Rome'. Recommended when the name is common.
`name`	Yes	Restaurant name to search for (partial matches work). Example: 'Barrafina', 'Pizza Pilgrims', 'Noma'.
`limit`	No	Maximum results to return (1-20, default 5).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	No
`message`	No
`results`	No
`total_matches`	No

TDQS

A4.3/5.0

Behavior5/5

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

The description goes beyond the readOnly and idempotent annotations by explaining the coverage_level values ('full', 'basic', 'none') and what each entails (letter grade vs preliminary band vs no analysis). This provides meaningful behavioral transparency about the varying quality of results.

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 earns its place: purpose, return value with downstream usage, parameter tip, and output semantics. Despite the detail, the description remains tight and front-loaded.

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 annotations covering read-only, idempotent, and non-destructive behavior, and an output schema presumably covering fields, the description still communicates the key result concept (Seemor IDs) and the coverage_level variants. Nothing an agent needs to use this tool 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% with detailed parameter descriptions. The description's use of the city parameter to disambiguate common names is also already present in the schema, so it adds no additional value beyond what structured data already provides.

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?

The description clearly states the tool finds restaurants by name and returns their Seemor IDs, which is a specific verb+resource. However, it does not distinguish itself from the sibling tool search_restaurants, so an agent may not know which to pick without checking details.

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 gives actionable guidance: use the city parameter to disambiguate common names and pass IDs to lookup_restaurant for full details. It doesn't mention when to prefer find_restaurant over search_restaurants or other siblings, but the provided context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Seemor Restaurant Intelligence tools Inspect

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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.

Resources

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