A
licenseNot graded
qualityC
maintenanceHybrid RAG pipeline that indexes documents and exposes them via an MCP server, enabling natural language queries to retrieve relevant context chunks for LLMs.
MIT
food-recipe-mcp
Server Details
Semantic search across 50,000+ food recipes with hybrid retrieval and reranking.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- AIDataNordic/Food-Recipe-MCP
- GitHub Stars
- 1
- Server Listing
- food-recipe-mcp
Available Tools
2 toolspingARead-onlyInspect
Simple connectivity test. Returns a greeting to confirm the server is running.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Arbitrary label included in the response, e.g. 'healthcheck' | world |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A3.7/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint=true, which already indicates a safe read operation. The description adds that it returns a greeting to confirm the server is running, but does not mention any rate limits, authentication, or error behavior. This is adequate given the annotation coverage.
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?
Description is two short sentences, front-loaded with the core purpose. No filler or redundant information.
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 simple ping tool with one optional parameter and an output schema, the description covers the tool's purpose and return behavior. Schema handles parameter semantics, annotations handle safety.
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 'name' parameter is fully documented in the input schema. The description does not add any extra parameter details, so baseline 3 is appropriate.
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 identifies the tool as a connectivity test that returns a greeting, using a specific verb ('returns') and resource ('server connectivity'). This distinguishes it from sibling tool search_recipes, which is for searching recipes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is given on when to use this tool versus alternatives. The sibling search_recipes is unrelated, but the description does not state when to prefer ping (e.g., for health checks before other calls).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_recipesARead-onlyInspect
Search a database of recipes using hybrid semantic search (dense + sparse) with reranking.
The database contains ~50,000 recipes from Food.com covering a wide range of cuisines, meal types, and cooking styles. Recipes include nutritional information, difficulty ratings, and user ratings.
Use natural language in the query to describe what you are looking for — cuisine, style, main ingredient, occasion, or mood all work well. Norwegian and English are both supported natively. Examples: 'quick Italian pasta for weeknight dinner' 'Swedish meatballs with gravy' 'healthy high-protein chicken bowl' 'easy chocolate cake for beginners' 'something with salmon and lemon' 'Indian curry chicken' 'traditional Norwegian kjøttkaker' 'hurtig pasta med kylling' 'enkel sjokoladekake'
Args: query: What you are looking for — describe the dish, cuisine, main ingredient, cooking style or mood freely. Any language is supported. diet: Optional — filter by dietary requirement: 'vegetarian', 'vegan', 'gluten-free', 'dairy-free', 'low-carb', 'keto', 'paleo' max_minutes: Optional — maximum total time in minutes, e.g. 30 difficulty: Optional — 'easy', 'medium' or 'hard' servings: Optional — not used for filtering (servings vary), but include in query for scaling context, e.g. 'pasta dish for 6 people' limit: Number of results to return after reranking (default 5, max 20)
Returns: List of recipes ranked by relevance. Each result includes rerank_score, rrf_score (hybrid fusion), title, total_time, difficulty, diet labels, ingredients, instructions, nutrition, rating, and source URL context.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| diet | No | Optional dietary filter: vegetarian, vegan, gluten-free, dairy-free, low-carb, keto, or paleo | |
| limit | No | Number of results to return after reranking (1–20, default 5) | |
| query | Yes | Natural language description of what you want, e.g. 'quick Italian pasta' or 'enkel sjokoladekake' | |
| servings | No | Not used for filtering — include serving size context in query instead, e.g. 'pasta for 6 people' | |
| difficulty | No | Optional difficulty filter: easy, medium, or hard | |
| max_minutes | No | Optional maximum total cooking time in minutes, e.g. 30. Use 0 for no limit |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 substantial behavioral traits: hybrid search mechanics (dense+sparse), reranking, database scope (~50,000 recipes), result fields (rerank_score, rrf_score, etc.), and the limitation that servings is not used for filtering. This far exceeds the annotation's minimal safety signal.
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 well-structured with clear sections (database context, usage examples, args, returns). It front-loads the core purpose and every sentence, including the seven query examples, adds practical value. The length is justified by the need to explain hybrid search, filters, and result contents.
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?
The description is fully self-contained: it explains the database scope, query language support, all parameters, return fields, and limitations. Even with an output schema available, it details what each result contains (rerank_score, rrf_score, ingredients, nutrition, etc.), making it complete for an agent to select and invoke correctly.
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%, but the description adds significant meaning beyond each parameter's schema description. For example, it clarifies that servings should be included in query context, states that query supports any language, and gives concrete example values for each parameter. This goes above the baseline for high schema coverage.
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 immediately states a specific verb+resource: 'Search a database of recipes using hybrid semantic search (dense + sparse) with reranking.' This clearly distinguishes the tool from the only sibling 'ping' and leaves no ambiguity about its function.
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 rich usage guidance, including natural language query examples, supported languages (Norwegian and English), and filter options. It also clarifies that 'servings' is not for filtering but should be included in the query. However, it does not explicitly state when not to use the tool or name alternatives beyond the sibling ping, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Frequently Asked Questions
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/.well-known/glama.jsonon 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_..."
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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.
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Discussions
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TDQS
A4.1/5.0
Disambiguation5/5
ping and search_recipes have completely distinct purposes: one is a health check, the other is the core search functionality. There is no overlap or ambiguity between them.
Naming Consistency5/5
Both tool names follow a simple, conventional lowercase verb style. 'ping' is a standard connectivity command, and 'search_recipes' follows the verb_noun pattern, which is consistent and predictable.
Tool Count3/5
With only 2 tools, the server feels thin for a large recipe database. The search tool is powerful, but a typical recipe server might also include get_recipe or list_categories. The count is on the low end of the borderline range.
Completeness4/5
For a read-only recipe search server, the surface is mostly complete. search_recipes returns full recipe details, so there is no need for separate get_recipe. Minor gaps include no direct recipe-by-ID retrieval and no browsing/filtering without a query, but these are workable for most use cases.