Seemor Restaurant Intelligence

Ask About Restaurant

ask_about_restaurant

Read-only

Ask a specific question about a restaurant based on analysis of real reviews and menu data. Common questions: what to order, group suitability, dietary options, vibe/atmosphere, value assessment. Requires a restaurant_id from find_restaurant or search_restaurants.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`question`	Yes	Question about the restaurant. Examples: 'What should I order?', 'Is it good for groups?', 'What are the dietary accommodations?', 'Is it worth the price?'
`restaurant_id`	Yes	Seemor restaurant ID (UUID). Get IDs from find_restaurant or search_restaurants.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`answer`	No
`source`	No
`status`	No
`message`	No
`category`	No
`question`	No
`seemor_url`	No
`restaurant_id`	No
`restaurant_name`	No

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds that answers are based on analysis of reviews and menu data, which gives insight into the tool's behavior without contradicting 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?

Two sentences, no wasted words. Essential information is front-loaded and efficiently communicated.

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?

Given the tool's simplicity, full schema coverage, presence of output schema, and clear annotations, the description is complete. It adds the necessary guidance on parameter sourcing and typical use cases.

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

Parameters4/5

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

Schema coverage is 100%, so both parameters are already documented. The description adds extra context: restaurant_id must come from specific tools, and provides example questions, improving agent understanding.

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 the tool asks questions about a restaurant using reviews and menu data, and provides concrete examples. It implicitly distinguishes from siblings like find_restaurant and recommend.

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?

The description explains that a restaurant_id from find_restaurant or search_restaurants is required and gives example questions, providing useful context for when to use. Does not explicitly state when not to use, but sufficiently 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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