Seemor Restaurant Intelligence

Ask About Restaurant

ask_about_restaurant

Read-onlyIdempotent

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. Ask one question per call. Accounts connected through an assistant have a small free number of answers per day; if the result carries limit_reached, relay its message to the user as written and do not retry.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "restaurant_id": "<restaurant_id>",
      "question": "<question>"
    },
    "name": "ask_about_restaurant"
  }
}

Input 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

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

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedNov 10, 2026
- addedOutput schema / properties / ai_generated
```
Added value: +{
+  "type": "boolean"
+}
```
- addedOutput schema / properties / digital_source_type
```
Added value: +{
+  "type": "string"
+}
```
Changed1 schema field changedNov 3, 2026
- addedOutput schema / properties / next_step
```
Added value: +{
+  "type": "string"
+}
```
Changed5 schema fields changedJul 30, 2026
- addedOutput schema / properties / category
```
Added value: +{
+  "type": "string"
+}
```
- addedOutput schema / properties / message
```
Added value: +{
+  "type": "string"
+}
```
- addedOutput schema / properties / restaurant_id
```
Added value: +{
+  "type": "string"
+}
```
- addedOutput schema / properties / seemor_url
```
Added value: +{
+  "type": "string"
+}
```
- addedOutput schema / properties / source
```
Added value: +{
+  "type": "string"
+}
```

Changed1 schema field changedJul 6, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "answer": {
+      "type": "string"
+    },
+    "question": {
+      "type": "string"
+    },
+    "restaurant_name": {
+      "type": "string"
+    },
+    "status": {
+      "type": "string"
+    }
+  },
+  "type": "object"
+}

First observedJul 6, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare this as a read-only, idempotent, closed-world read, so the description adds real value beyond them: the answer is derived from reviews and menu data, one question per call, and a free daily quota whose limit_reached result must be relayed verbatim without retry. The quota-and-no-retry instruction is a behavioral trait no annotation conveys.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then prerequisites, then quota handling – a sensible order with no filler sentences. The 'Common questions' list partially repeats the question examples already in the schema, which is the only mild redundancy.

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?

For a two-parameter read tool with an output schema, everything an agent needs is present: where to get the ID, how many questions per call, and what to do when the rate limit result appears. Return-value details are correctly left to the output schema.

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 the schema already documents both parameters and the baseline would be 3. The description goes slightly beyond by constraining the question parameter to a single question per call and by framing the categories of answerable questions, though the example questions largely duplicate the schema's own examples.

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?

States a specific verb and resource ('Ask a specific question about a restaurant') and qualifies the answer source ('based on analysis of real reviews and menu data'). It also enumerates the common question categories, so an agent knows this is a review-grounded Q&A tool rather than a raw-detail lookup like lookup_restaurant.

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?

Gives a clear precondition (requires a restaurant_id from find_restaurant or search_restaurants) and an operational rule ('Ask one question per call'), which is exactly the kind of when-to-use guidance an agent needs. It stops short of explicitly excluding alternatives such as lookup_restaurant for factual fields, so it is strong but not fully exhaustive.

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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