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 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.

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?

Annotations cover safety (readOnlyHint=true, destructiveHint=false) but say nothing about throttling; the description adds a free-answer-per-day quota and a specific error-handling protocol (relay limit_reached, do not retry). That is real behavioral context the structured fields do not carry.

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?

Front-loaded with purpose, then categories, then prerequisites, then limits. Every sentence carries actionable information – no filler or restatement of the name.

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 an output schema present and annotations covering safety, the description need not explain return values. Given the rate-limit disclosure and prerequisite routing, it is complete for what an agent needs to invoke correctly.

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 coverage is 100%, so the schema already documents both parameters and their examples. The description reinforces the restaurant_id provenance but adds no syntax or format detail beyond the schema; baseline 3 applies.

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 (ask) and resource (question about a restaurant), plus the basis of the answer (real reviews and menu data). The scope 'one question per call' and the concrete question categories make it unmistakable against siblings like recommend or 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states the prerequisite ('Requires a restaurant_id from find_restaurant or search_restaurants'), the usage cadence ('Ask one question per call'), and how to handle the limit_reached outcome. It routes the agent to the right upstream sibling and gives the when-not via the rate-limit rule.

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

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?