Seemor Restaurant Intelligence

Explore Area

explore_area

Read-onlyIdempotent

Get aggregate dining statistics for a neighborhood, city, or region: cuisine breakdown, grade distribution, price range, top neighborhoods, and highlighted restaurants. Use this for area-level context ("what is the dining scene like in Shoreditch?"), NOT for finding a specific restaurant or getting a personal recommendation. For "find me a quiet Italian near Shoreditch", use the recommend tool instead. Coverage is reported honestly: coverage_level 'full' (fully analyzed rows present), 'basic' (only quick-read rows: review-analysis bands, no letter grades yet), or 'none'; analyzed_count and preliminary_count split total_restaurants, and highlights marked coverage_level 'basic' carry a preliminary_band instead of a grade. not_found means neither analyzed nor preliminary rows exist.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`area`	Yes	Neighborhood, city, or region name. Supports 'Shoreditch', 'Shoreditch, London', 'Central London', 'Rome', 'Chelsea, NYC'. Use comma to disambiguate neighborhoods that exist in multiple cities.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`area`	No
`status`	No
`message`	No
`highlights`	No
`description`	No
`top_cuisines`	No
`neighborhoods`	No
`analyzed_count`	No
`coverage_level`	No
`price_breakdown`	No
`preliminary_count`	No
`total_restaurants`	No
`grade_distribution`	No
`analyzed_restaurants`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedAug 22, 2026
- changedOutput schema / properties / highlights / items / properties / coverage_level / enum
```
Previous value: -[
-  "full",
-  "basic"
-]New value: +[
+  "full",
+  "basic",
+  "none"
+]
```

Changed6 schema fields changedAug 22, 2026

addedOutput schema / properties / analyzed_count
```
Added value: +{
+  "type": "number"
+}
```

addedOutput schema / properties / coverage_level

Added value: +{
+  "enum": [
+    "full",
+    "basic",
+    "none"
+  ],
+  "type": "string"
+}

changedOutput schema / properties / description / type

Previous value: -"string"New value: +[
+  "string",
+  "null"
+]

addedOutput schema / properties / highlights / items / properties

Added value: +{
+  "address": {
+    "type": [
+      "string",
+      "null"
+    ]
+  },
+  "caveats": {
+    "items": {
+      "type": "string"
+    },
+    "type": "array"
+  },
+  "city": {
+    "type": [
+      "string",
+      "null"
+    ]
+  },
+  "coverage_level": {
+    "enum": [
+      "full",
+      "basic"
+    ],
+    "type": "string"
+  },
+  "cuisine_tags": {
+    "items": {
+      "type": "string"
+    },
+    "type": "array"
+  },
+  "distance_km": {
+    "type": [
+      "number",
+      "null"
+    ]
+  },
+  "grade": {
+    "type": [
+      "string",
+      "null"
+    ]
+  },
+  "name": {
+    "type": "string"
+  },
+  "neighborhood": {
+    "type": [
+      "string",
+      "null"
+    ]
+  },
+  "preliminary_band": {
+    "type": [
+      "string",
+      "null"
+    ]
+  },
+  "price_level": {
+    "type": [
+      "string",
+      "null"
+    ]
+  },
+  "seemor_id": {
+    "type": "string"
+  },
+  "tldr": {
+    "type": [
+      "string",
+      "null"
+    ]
+  }
+}

addedOutput schema / properties / preliminary_count

Added value: +{
+  "type": [
+    "number",
+    "null"
+  ]
+}

addedOutput schema / properties / status / enum

Added value: +[
+  "ok",
+  "error",
+  "not_found"
+]

Changed4 schema fields changedJul 30, 2026
- addedOutput schema / properties / description
```
Added value: +{
+  "type": "string"
+}
```
- addedOutput schema / properties / message
```
Added value: +{
+  "type": "string"
+}
```
- addedOutput schema / properties / neighborhoods
```
Added value: +{
+  "items": {
+    "type": "object"
+  },
+  "type": "array"
+}
```
- addedOutput schema / properties / price_breakdown
```
Added value: +{
+  "type": "object"
+}
```

Changed1 schema field changedJul 6, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "analyzed_restaurants": {
+      "type": "number"
+    },
+    "area": {
+      "type": "string"
+    },
+    "grade_distribution": {
+      "type": "object"
+    },
+    "highlights": {
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "status": {
+      "type": "string"
+    },
+    "top_cuisines": {
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total_restaurants": {
+      "type": "number"
+    }
+  },
+  "type": "object"
+}

First observedJul 6, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: it explains the coverage_level values ('full', 'basic', 'none') and what they mean, how analyzed_count and preliminary_count split total_restaurants, and that highlights with coverage_level 'basic' carry a preliminary_band instead of a grade. It also explains the not_found case. This is rich behavioral disclosure that goes beyond the annotations. The only minor gap is not describing the exact response structure, but the output schema exists and covers that.

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?

The description is a single paragraph but packs a lot of information efficiently. It front-loads the core purpose and outputs, then covers usage guidance, coverage semantics, and edge cases. It's slightly dense but every sentence earns its place. The only minor deduction is that the coverage explanation could be broken into clearer sentences, but it's not bloated or redundant.

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 complexity (coverage levels, counts, preliminary bands, not_found case) and the fact that an output schema exists, the description is remarkably complete. It explains all the non-obvious behaviors an agent needs to interpret results correctly: what each coverage_level means, how counts split, what preliminary_band implies, and the not_found case. The single parameter is fully documented in the schema. Nothing critical is missing for an agent to call this tool correctly and interpret its output.

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 description coverage is 100%, so the schema already documents the 'area' parameter well, including examples and disambiguation guidance. The description adds value by framing the parameter in the context of the tool's purpose (area-level context) and reinforcing the disambiguation advice with the example 'Shoreditch, London'. Since the schema does the heavy lifting, a baseline of 3 applies, but the description's reinforcement of the comma-disambiguation pattern and the example queries earn a 4.

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's purpose: 'Get aggregate dining statistics for a neighborhood, city, or region' and lists specific outputs (cuisine breakdown, grade distribution, price range, top neighborhoods, highlighted restaurants). It distinguishes itself from siblings by explicitly stating it is NOT for finding a specific restaurant or personal recommendation, and names the alternative (recommend tool) for that use case.

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?

The description provides explicit when-to-use guidance: 'Use this for area-level context' with an example query, and when-not-to-use: 'NOT for finding a specific restaurant or getting a personal recommendation.' It also names the alternative tool ('use the recommend tool instead') and gives a concrete example of when to use that alternative. This is exemplary usage guidance.

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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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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