Structured restaurant intelligence for AI platforms. 760K+ restaurants catalogued across 26 countries, 62K+ with deep 37-dimension analysis including letter grades, occasion-aware recommendations, menu insights, and neighborhood exploration. Six tools: find restaurants by name, search by location, explore area dining scenes, look up detailed profiles (3 detail tiers), ask natural language questions, and get personalized recommendations.
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.
| 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. |
| Name | Required | Description |
|---|---|---|
| answer | No | |
| source | No | |
| status | No | |
| message | No | |
| category | No | |
| question | No | |
| seemor_url | No | |
| restaurant_id | No | |
| restaurant_name | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds that answers are based on analysis of reviews and menu data, which gives insight into the tool's behavior without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. Essential information is front-loaded and efficiently communicated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, full schema coverage, presence of output schema, and clear annotations, the description is complete. It adds the necessary guidance on parameter sourcing and typical use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so both parameters are already documented. The description adds extra context: restaurant_id must come from specific tools, and provides example questions, improving agent understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool asks questions about a restaurant using reviews and menu data, and provides concrete examples. It implicitly distinguishes from siblings like find_restaurant and recommend.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that a restaurant_id from find_restaurant or search_restaurants is required and gives example questions, providing useful context for when to use. Does not explicitly state when not to use, but sufficiently clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| 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. |
| Name | Required | Description |
|---|---|---|
| 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 |
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.
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.
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.
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.
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.
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.
Find restaurants by name. Returns matching restaurants with their Seemor IDs, which can be passed to lookup_restaurant for full details. Use the city parameter to disambiguate common names. Each result reports coverage_level: 'full' (letter grade), 'basic' (preliminary read: grade null, preliminary_band, tldr, caveat) or 'none' (no analysis yet).
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | City to narrow the search. Example: 'London', 'New York', 'Rome'. Recommended when the name is common. | |
| name | Yes | Restaurant name to search for (partial matches work). Example: 'Barrafina', 'Pizza Pilgrims', 'Noma'. | |
| limit | No | Maximum results to return (1-20, default 5). |
| Name | Required | Description |
|---|---|---|
| status | No | |
| message | No | |
| results | No | |
| total_matches | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the readOnly and idempotent annotations by explaining the coverage_level values ('full', 'basic', 'none') and what each entails (letter grade vs preliminary band vs no analysis). This provides meaningful behavioral transparency about the varying quality of results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Every sentence earns its place: purpose, return value with downstream usage, parameter tip, and output semantics. Despite the detail, the description remains tight and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With annotations covering read-only, idempotent, and non-destructive behavior, and an output schema presumably covering fields, the description still communicates the key result concept (Seemor IDs) and the coverage_level variants. Nothing an agent needs to use this tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with detailed parameter descriptions. The description's use of the city parameter to disambiguate common names is also already present in the schema, so it adds no additional value beyond what structured data already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds restaurants by name and returns their Seemor IDs, which is a specific verb+resource. However, it does not distinguish itself from the sibling tool search_restaurants, so an agent may not know which to pick without checking details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives actionable guidance: use the city parameter to disambiguate common names and pass IDs to lookup_restaurant for full details. It doesn't mention when to prefer find_restaurant over search_restaurants or other siblings, but the provided context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| 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. |
| Name | Required | Description |
|---|---|---|
| status | No | |
| message | No | |
| restaurant | No |
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.
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.
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.
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.
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.
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.
Returns a complete, ranked restaurant recommendation for a dining occasion: the final answer, not a search. Each result already carries a grade (or preliminary_band), match_reasons, and caveats weighed against the request. Call it ONCE per question, with the full ask (cuisine, occasion, vibe, constraints) in query and the place in location. Do not re-call with reworded variations or call lookup_restaurant on each result to double-check it; that adds latency, not a better answer. If a result carries caveats, coverage_level 'basic', or a message noting a thin pool, relay it to the user instead of searching again. Requires a location: include one in your query (e.g. "in Soho"), or provide location, or latitude/longitude, or the tool refuses with location_required instead of guessing a city.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of recommendations to return (1-10, default 5). | |
| query | Yes | Natural language dining request, e.g. 'quiet Italian restaurant for a date night in Covent Garden' or 'best sushi near me for a celebration'. Include the location in your query OR provide latitude/longitude. | |
| latitude | No | Latitude of search center. Alternative to location — use when you have coordinates. If both location and lat/lng are provided, location takes priority for disambiguation. | |
| location | No | City or area to bias the search toward, e.g. 'London', 'San Francisco', 'Rome'. Use this when the query doesn't include a location, or to disambiguate (e.g. 'Victoria' could be London or British Columbia — pass 'London' to clarify). If omitted and the query contains a location, that location is used. | |
| longitude | No | Longitude of search center. Must be provided with latitude. |
| Name | Required | Description |
|---|---|---|
| status | No | |
| message | No | |
| results | No | |
| location_used | No | |
| pool_disclosure | No | |
| query_understood | No | |
| total_candidates | No | |
| unverifiable_attributes | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, non-destructive behavior, and the description adds meaningful context: results are pre-scored with grades and caveats, re-calling only 'adds latency, not a better answer', and missing location causes a location_required refusal instead of a guessed city. No contradiction with 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The main purpose is front-loaded in the first sentence, and each subsequent sentence carries distinct usage or behavioral guidance. The length is warranted by the number of common misuse patterns the description proactively prevents, with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Combined with the output schema and annotations, the description covers everything needed for correct invocation: call budget, query and location relationship, error mode, and how to handle low-confidence or caveated results. Nothing essential is left implicit.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents all five parameters with full coverage, so the baseline is 3. The description adds value by defining the query as the full ask and clarifying the location requirement and refusal behavior, while leaving limit and coordinate details to the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Opens with a precise deliverable: 'Returns a complete, ranked restaurant recommendation for a dining occasion: the final answer, not a search.' This clearly distinguishes the tool from search and lookup siblings, and tells the agent this is the final recommendation rather than a list of candidates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives explicit when and how to use it: call once per question with the full ask, include the place in location, and do not re-call with reworded variations. It also explicitly warns against calling lookup_restaurant to double-check results and instructs relaying caveats instead of searching again, which directly handles the likely alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for restaurants near a location. Returns graded, ranked results with cuisine, price level, and Seemor analysis summaries. Fully analyzed restaurants (coverage_level 'full', letter grade) come first; when fewer than limit are available, quick-read restaurants are appended after them, marked coverage_level 'basic' with grade null and a preliminary_band (e.g. 'A-range') graded from review analysis, not star ratings. total_in_area = analyzed_in_area + preliminary_in_area; a message explains when quick-read rows are included.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | Sort order: "grade" (default, best first) or "distance" (nearest first). | |
| limit | No | Maximum results to return. Default 10, max 10. | |
| cuisine | No | Filter by cuisine type (e.g. "Italian", "Japanese"). Case-insensitive substring match. | |
| latitude | Yes | Latitude of the search center (-90 to 90). | |
| longitude | Yes | Longitude of the search center (-180 to 180). | |
| min_grade | No | Minimum letter grade to include (e.g. "B+"). Grades: A+, A, A-, B+, B, B-, C+, C, C-, D, F. | |
| radius_km | No | Search radius in kilometers. Default 2, max 10. | |
| price_level | No | Filter by price level: "$", "$$", "$$$", or "$$$$". |
| Name | Required | Description |
|---|---|---|
| status | No | |
| message | No | |
| results | No | |
| total_in_area | No | |
| analyzed_in_area | No | |
| preliminary_in_area | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the safety profile is covered. The description adds substantial behavioral context: the two-tier result ordering (full-analyzed first, basic appended), the grading semantics (grade null for basic, preliminary_band from review analysis), and the total_in_area formula. It also discloses the 'message explains when quick-read rows are included'. That's meaningful behavior beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense paragraph but every sentence earns its place: it states the purpose, the result ranking, the two-tier coverage model, the meaning of total_in_area, and the messaging behavior. It's front-loaded with the core function and then layers specifics. For a tool with 8 parameters, it's appropriately sized without fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 params, output schema exists), the description covers the key behavioral nuances: ordering, coverage levels, how grades are assigned, and the total formula. It doesn't explain return fields in detail, but there's an output schema that likely carries that. It could mention pagination or max values, but those are in the schema. The description is complete enough for an agent to call correctly and interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all 8 parameters are already documented with types and descriptions. The tool description adds a bit around limit (when fewer than limit are available), and it mentions 'preliminary_band' which relates to grading but doesn't add new parameter semantics. It also implies the default sort is 'grade', which matches the schema's description. Since the schema carries the load, baseline 3 is appropriate; the description doesn't add extra param-level meaning beyond what's in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description says 'Search for restaurants near a location' (verb+resource+scope), and then details exactly what results it returns: graded, ranked results with cuisine, price, and analysis. It distinguishes itself from sibling tools: it's the search tool, while find_restaurant and lookup_restaurant are probably direct lookups, explore_area is area-focused, recommend is recommendation. The specific mention of the two-tier coverage level is a clear differentiator.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It says 'when fewer than limit are available, quick-read restaurants are appended' — that's a when/condition for the inclusion of basic rows. It does not explicitly name sibling tools or contrast alternatives, but it gives clear operational guidance on how result sets behave, which helps an agent decide when to use it and how to interpret results. No explicit exclusions or alternates, but the context is clear for a search tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
recommend3 fields changedOutput schema / properties / pool_disclosureAdded value: +{
+ "properties": {
+ "count": {
+ "type": "number"
+ },
+ "message": {
+ "type": "string"
+ },
+ "reason": {
+ "enum": [
+ "thin_pool",
+ "cuisine_relaxed"
+ ],
+ "type": "string"
+ }
+ },
+ "type": [
+ "object",
+ "null"
+ ]
+}Output schema / properties / status / enumPrevious value: -[
- "ok",
- "error",
- "no_coverage",
- "location_required",
- "timeout"
-]New value: +[
+ "ok",
+ "error",
+ "no_coverage",
+ "location_required",
+ "timeout",
+ "cannot_verify"
+]Output schema / properties / unverifiable_attributesAdded value: +{
+ "items": {
+ "properties": {
+ "key": {
+ "type": "string"
+ },
+ "label": {
+ "type": "string"
+ },
+ "source_phrase": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": [
+ "array",
+ "null"
+ ]
+}explore_area1 field changedOutput schema / properties / highlights / items / properties / coverage_level / enumPrevious value: -[
- "full",
- "basic"
-]New value: +[
+ "full",
+ "basic",
+ "none"
+]find_restaurant1 field changedOutput schema / properties / results / items / properties / coverage_level / enumPrevious value: -[
- "full",
- "basic"
-]New value: +[
+ "full",
+ "basic",
+ "none"
+]lookup_restaurant8 fields changedOutput schema / properties / messageAdded value: +{
+ "type": "string"
+}Output schema / properties / restaurant / properties / addressAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / restaurant / properties / analysis_dateAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / restaurant / properties / caveatsAdded value: +{
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}Output schema / properties / restaurant / properties / cityAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / restaurant / properties / coverage_levelAdded value: +{
+ "enum": [
+ "full",
+ "basic",
+ "none"
+ ],
+ "type": "string"
+}Output schema / properties / restaurant / properties / preliminary_bandAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / restaurant / properties / seemor_urlAdded value: +{
+ "type": "string"
+}recommend1 field changedOutput schema / properties / results / items / properties / coverage_level / enumPrevious value: -[
- "full",
- "basic"
-]New value: +[
+ "full",
+ "basic",
+ "none"
+]search_restaurants1 field changedOutput schema / properties / results / items / properties / coverage_level / enumPrevious value: -[
- "full",
- "basic"
-]New value: +[
+ "full",
+ "basic",
+ "none"
+]explore_area6 fields changedOutput schema / properties / analyzed_countAdded value: +{
+ "type": "number"
+}Output schema / properties / coverage_levelAdded value: +{
+ "enum": [
+ "full",
+ "basic",
+ "none"
+ ],
+ "type": "string"
+}Output schema / properties / description / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / highlights / items / propertiesAdded 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"
+ ]
+ }
+}Output schema / properties / preliminary_countAdded value: +{
+ "type": [
+ "number",
+ "null"
+ ]
+}Output schema / properties / status / enumAdded value: +[
+ "ok",
+ "error",
+ "not_found"
+]find_restaurant10 fields changedOutput schema / properties / results / items / properties / address / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / results / items / properties / caveatsAdded value: +{
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}Output schema / properties / results / items / properties / city / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / results / items / properties / coverage_level / enumAdded value: +[
+ "full",
+ "basic"
+]Output schema / properties / results / items / properties / distance_kmAdded value: +{
+ "type": [
+ "number",
+ "null"
+ ]
+}Output schema / properties / results / items / properties / grade / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / results / items / properties / neighborhood / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / results / items / properties / preliminary_bandAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / results / items / properties / price_levelAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / results / items / properties / tldrAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}lookup_restaurant5 fields changedOutput schema / properties / restaurant / properties / grade / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / restaurant / properties / grade_label / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / restaurant / properties / neighborhood / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / restaurant / properties / price_level / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / restaurant / properties / tldr / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]recommend4 fields changedOutput schema / properties / results / items / properties / coverage_levelAdded value: +{
+ "enum": [
+ "full",
+ "basic"
+ ],
+ "type": "string"
+}Output schema / properties / results / items / properties / grade / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / results / items / properties / preliminary_bandAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / status / enumPrevious value: -[
- "ok",
- "error",
- "no_coverage",
- "location_required"
-]New value: +[
+ "ok",
+ "error",
+ "no_coverage",
+ "location_required",
+ "timeout"
+]search_restaurants12 fields changedOutput schema / properties / analyzed_in_areaAdded value: +{
+ "type": "number"
+}Output schema / properties / preliminary_in_areaAdded value: +{
+ "type": "number"
+}Output schema / properties / results / items / properties / address / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / results / items / properties / caveatsAdded value: +{
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}Output schema / properties / results / items / properties / city / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / results / items / properties / coverage_level / enumAdded value: +[
+ "full",
+ "basic"
+]Output schema / properties / results / items / properties / distance_kmAdded value: +{
+ "type": [
+ "number",
+ "null"
+ ]
+}Output schema / properties / results / items / properties / grade / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / results / items / properties / neighborhood / typePrevious value: -"string"New value: +[
+ "string",
+ "null"
+]Output schema / properties / results / items / properties / preliminary_bandAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / results / items / properties / price_levelAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / results / items / properties / tldrAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}recommend1 field changedOutput schema / properties / results / items / properties / caveatsAdded value: +{
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}ask_about_restaurant5 fields changedOutput schema / properties / categoryAdded value: +{
+ "type": "string"
+}Output schema / properties / messageAdded value: +{
+ "type": "string"
+}Output schema / properties / restaurant_idAdded value: +{
+ "type": "string"
+}Output schema / properties / seemor_urlAdded value: +{
+ "type": "string"
+}Output schema / properties / sourceAdded value: +{
+ "type": "string"
+}explore_area4 fields changedOutput schema / properties / descriptionAdded value: +{
+ "type": "string"
+}Output schema / properties / messageAdded value: +{
+ "type": "string"
+}Output schema / properties / neighborhoodsAdded value: +{
+ "items": {
+ "type": "object"
+ },
+ "type": "array"
+}Output schema / properties / price_breakdownAdded value: +{
+ "type": "object"
+}find_restaurant2 fields changedOutput schema / properties / messageAdded value: +{
+ "type": "string"
+}Output schema / properties / total_matchesAdded value: +{
+ "type": "number"
+}recommend7 fields changedOutput schema / properties / location_usedAdded value: +{
+ "type": "string"
+}Output schema / properties / messageAdded value: +{
+ "type": "string"
+}Output schema / properties / query_understoodAdded value: +{
+ "type": "string"
+}Output schema / properties / recommendationsRemoved value: -{
- "items": {
- "properties": {
- "grade": {
- "type": "string"
- },
- "match_reasons": {
- "items": {
- "type": "string"
- },
- "type": "array"
- },
- "name": {
- "type": "string"
- },
- "relevance": {
- "type": "string"
- },
- "seemor_id": {
- "type": "string"
- }
- },
- "type": "object"
- },
- "type": "array"
-}Output schema / properties / resultsAdded value: +{
+ "items": {
+ "properties": {
+ "distance_km": {
+ "type": [
+ "number",
+ "null"
+ ]
+ },
+ "grade": {
+ "type": "string"
+ },
+ "match_reasons": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "name": {
+ "type": "string"
+ },
+ "relevance": {
+ "type": "string"
+ },
+ "seemor_id": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+}Output schema / properties / status / enumAdded value: +[
+ "ok",
+ "error",
+ "no_coverage",
+ "location_required"
+]Output schema / properties / total_candidatesAdded value: +{
+ "type": "number"
+}search_restaurants3 fields changedOutput schema / properties / messageAdded value: +{
+ "type": "string"
+}Output schema / properties / totalRemoved value: -{
- "type": "number"
-}Output schema / properties / total_in_areaAdded value: +{
+ "type": "number"
+}lookup_restaurant1 field changedInput schema / properties / fields / descriptionPrevious 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."ask_about_restaurant1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "answer": {
+ "type": "string"
+ },
+ "question": {
+ "type": "string"
+ },
+ "restaurant_name": {
+ "type": "string"
+ },
+ "status": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+}explore_area1 field 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"
+}find_restaurant1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "results": {
+ "items": {
+ "properties": {
+ "address": {
+ "type": "string"
+ },
+ "city": {
+ "type": "string"
+ },
+ "coverage_level": {
+ "type": "string"
+ },
+ "cuisine_tags": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "grade": {
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ },
+ "neighborhood": {
+ "type": "string"
+ },
+ "seemor_id": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "status": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+}lookup_restaurant1 field 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"
+}recommend1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "recommendations": {
+ "items": {
+ "properties": {
+ "grade": {
+ "type": "string"
+ },
+ "match_reasons": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "name": {
+ "type": "string"
+ },
+ "relevance": {
+ "type": "string"
+ },
+ "seemor_id": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "status": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+}search_restaurants1 field changedOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "results": {
+ "items": {
+ "properties": {
+ "address": {
+ "type": "string"
+ },
+ "city": {
+ "type": "string"
+ },
+ "coverage_level": {
+ "type": "string"
+ },
+ "cuisine_tags": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "grade": {
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ },
+ "neighborhood": {
+ "type": "string"
+ },
+ "seemor_id": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "status": {
+ "type": "string"
+ },
+ "total": {
+ "type": "number"
+ }
+ },
+ "type": "object"
+}ask_about_restaurantexplore_areafind_restaurantlookup_restaurantrecommendsearch_restaurantsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Add one secure layer between your agents and this server.
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.
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.
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.
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.