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. Ask one question per call. Accounts connected through Claude 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.

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

Schema Changelog

Changes observed during successful MCP inspections.

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

A4.7/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses that answers come from analysis of real reviews and menu data, that there is a daily free allowance for connected accounts, and that limit_reached must be relayed verbatim without retry. These are behavioral traits that materially affect agent behavior and are not present in 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 four sentences, tightly packed: purpose/data basis, example questions, prerequisite and question constraint, and rate-limit handling. No filler or redundancy; each sentence contributes unique guidance.

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 tool with an output schema and annotations, the description covers the essential usage flow (obtain restaurant_id, ask one question), the data source, and the rate-limit behavior. The output schema handles return value details, so nothing an agent needs to invoke correctly is missing.

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% and the schema already documents both parameters with examples. The description adds extra meaning by listing common question types (what to order, group suitability, dietary options) and explicitly constraining the call to one question, which clarifies the expected cardinality of the question parameter beyond 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?

The description states a specific verb and resource: ask a specific question about a restaurant, and grounds it in analysis of real reviews and menu data. This clearly differentiates it from siblings like find_restaurant, search_restaurants, and recommend by focusing on post-discovery Q&A about a known 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?

The description provides clear use context: requires a restaurant_id from find_restaurant or search_restaurants, limits to one question per call, and gives explicit handling for the limit_reached result (relay message, do not retry). It does not explicitly name alternatives or state when not to use the tool, but the prerequisite and constraints make the intended workflow clear.

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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