Seemor Restaurant Intelligence

Look Up Restaurant

lookup_restaurant

Read-onlyIdempotent

Look up a specific restaurant by its Seemor ID. Returns grade, summary, cuisine, neighborhood, and other details. Use the fields parameter to request richer data (standard or premium; fully analyzed restaurants only). coverage_level 'full' rows carry a letter grade; 'basic' rows are Seemor quick reads: review-analysis bands (grade null, preliminary_band such as 'B-range') with a one-line tldr, graded from review analysis rather than star ratings; 'none' rows have no analysis yet. Use search_restaurants or find_restaurant first to get restaurant IDs. Use this for a single place the user asks about, not for every result of recommend.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`fields`	No	Response detail level. 'basic' (default): grade, TL;DR, cuisine, price. 'standard': adds narrative summary, occasion fit, menu highlights, cost estimates, dietary info. 'premium': adds dimensional assessments (noise, formality, authenticity, etc.), value assessment, standout strengths/weaknesses, unique selling points.
`restaurant_id`	Yes	Seemor restaurant ID (UUID). Get IDs from search_restaurants results.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	No
`message`	No
`restaurant`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed8 schema fields changedAug 22, 2026
- addedOutput schema / properties / message
```
Added value: +{
+  "type": "string"
+}
```
- addedOutput schema / properties / restaurant / properties / address
```
Added value: +{
+  "type": [
+    "string",
+    "null"
+  ]
+}
```
- addedOutput schema / properties / restaurant / properties / analysis_date
```
Added value: +{
+  "type": [
+    "string",
+    "null"
+  ]
+}
```
- addedOutput schema / properties / restaurant / properties / caveats
```
Added value: +{
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
```
- addedOutput schema / properties / restaurant / properties / city
```
Added value: +{
+  "type": [
+    "string",
+    "null"
+  ]
+}
```
- addedOutput schema / properties / restaurant / properties / coverage_level
```
Added value: +{
+  "enum": [
+    "full",
+    "basic",
+    "none"
+  ],
+  "type": "string"
+}
```
- addedOutput schema / properties / restaurant / properties / preliminary_band
```
Added value: +{
+  "type": [
+    "string",
+    "null"
+  ]
+}
```
- addedOutput schema / properties / restaurant / properties / seemor_url
```
Added value: +{
+  "type": "string"
+}
```
Changed5 schema fields changedAug 22, 2026
- changedOutput schema / properties / restaurant / properties / grade / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
- changedOutput schema / properties / restaurant / properties / grade_label / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
- changedOutput schema / properties / restaurant / properties / neighborhood / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
- changedOutput schema / properties / restaurant / properties / price_level / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```
- changedOutput schema / properties / restaurant / properties / tldr / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```

Changed1 schema field changedJul 13, 2026

changedInput schema / properties / fields / description

Previous value: -"Response detail level. 'basic' (default): grade, TL;DR, cuisine, price. 'standard': adds narrative summary, occasion fit, menu highlights, cost estimates, dietary info. 'premium': adds dimensional scores (noise, formality, authenticity, etc.), value assessment, strengths/weaknesses."New value: +"Response detail level. 'basic' (default): grade, TL;DR, cuisine, price. 'standard': adds narrative summary, occasion fit, menu highlights, cost estimates, dietary info. 'premium': adds dimensional assessments (noise, formality, authenticity, etc.), value assessment, standout strengths/weaknesses, unique selling points."

Changed1 schema field changedJul 6, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "restaurant": {
+      "properties": {
+        "cuisine_tags": {
+          "items": {
+            "type": "string"
+          },
+          "type": "array"
+        },
+        "grade": {
+          "type": "string"
+        },
+        "grade_label": {
+          "type": "string"
+        },
+        "name": {
+          "type": "string"
+        },
+        "neighborhood": {
+          "type": "string"
+        },
+        "price_level": {
+          "type": "string"
+        },
+        "seemor_id": {
+          "type": "string"
+        },
+        "tldr": {
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
+    "status": {
+      "type": "string"
+    }
+  },
+  "type": "object"
+}

First observedJul 6, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already establish read-only/idempotent behavior, and the description adds non-obvious output semantics: coverage_level 'full' rows have letter grades, 'basic' rows use preliminary_band with grade null and are based on review analysis, and 'none' rows have no analysis. This warns agents about nulls and data quality without contradicting the 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?

Every sentence carries distinct useful information: purpose, return contents, field semantics, coverage caveats, ID acquisition, and selection guidance. The detail on coverage is dense but organized and directly relevant, with no filler.

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 a good output schema and strong annotations, the description's added context covers prerequisite ID discovery, data-availability edge cases through coverage_level, and the appropriate scope vs siblings. Nothing needed to invoke the tool 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 enum values are already documented, so the baseline is 3. The description adds value by tying fields/coverage levels to data availability ('fully analyzed restaurants only') and by instructing where restaurant_id comes from, which lifts it above schema-only documentation.

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 opens with a specific verb and resource: 'Look up a specific restaurant by its Seemor ID,' and enumerates the returned details. It also distinguishes itself from siblings by directing users to search_restaurants/find_restaurant first and by noting it is for a single place, not for every recommend result.

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?

It explicitly states when to use the tool (single restaurant by known ID), what to do first (use search_restaurants or find_restaurant to get IDs), and when not to use it ('not for every result of recommend'). It also explains when fields=standard/premium is appropriate via the 'fully analyzed restaurants only' qualifier.

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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