food-recipe-mcp
Server Details
Semantic search across 50,000+ food recipes with hybrid retrieval and reranking.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- AIDataNordic/Food-Recipe-MCP
- GitHub Stars
- 0
- Server Listing
- food-recipe-mcp
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Tool Definition Quality
Average 4.5/5 across 2 of 2 tools scored.
The two tools have completely distinct purposes: 'ping' is a connectivity test with no domain functionality, while 'search_recipes' is the core domain operation for recipe discovery. There is zero overlap or ambiguity between them.
Both tools use snake_case naming, which is consistent. However, 'ping' follows a generic utility pattern while 'search_recipes' follows a descriptive verb_noun pattern - this minor deviation prevents a perfect score despite the consistent casing.
With only 2 tools, this server feels severely underpowered for a recipe domain. While 'search_recipes' is comprehensive, there are no tools for viewing individual recipes, saving favorites, managing collections, or other expected recipe-related operations that would justify such a minimal toolset.
The server provides excellent search capabilities but lacks fundamental recipe operations. There's no way to retrieve a specific recipe by ID, save/bookmark recipes, get recipe details without searching, or perform any CRUD operations. This creates significant gaps that will limit agent effectiveness beyond basic discovery.
Available Tools
2 toolspingAInspect
Simple connectivity test. Returns a greeting to confirm the server is running.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | world |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's a read-only operation (returns a greeting), has no destructive effects, and serves as a diagnostic tool. However, it doesn't mention potential rate limits, authentication needs, or error conditions.
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 concise sentences with zero waste. The first states the purpose, the second explains the return value and outcome. Perfectly front-loaded and appropriately sized for a simple tool.
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 low complexity (1 optional parameter), no annotations, but with an output schema (which handles return value documentation), the description is complete enough. It covers purpose, behavior, and parameter semantics adequately for this diagnostic tool.
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 has 0% description coverage (no parameter descriptions), but the description compensates by explaining the parameter's purpose: the 'name' parameter customizes the greeting ('Returns a greeting'). This adds meaningful context beyond the bare schema, though it doesn't specify format constraints.
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 with specific verbs ('connectivity test', 'Returns a greeting') and resource ('server'), distinguishing it from the sibling 'search_recipes' tool. It explains both the action (test connectivity) and outcome (confirm server is running).
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 implies usage context ('to confirm the server is running') but doesn't explicitly state when to use this versus alternatives or provide exclusions. No guidance is given about when not to use it or how it differs from other connectivity tests.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_recipesAInspect
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. Queries in any language are supported and will be automatically translated to English before search. 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.
| Name | Required | Description | Default |
|---|---|---|---|
| diet | No | ||
| limit | No | ||
| query | Yes | ||
| servings | No | ||
| difficulty | No | ||
| max_minutes | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: the database size (~50,000 recipes), multilingual query support with auto-translation, hybrid search methodology (dense + sparse with reranking), and result limit constraints (default 5, max 20). It doesn't mention rate limits or authentication requirements.
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 well-structured with clear sections (overview, database context, usage guidance, examples, parameter explanations, return format). Every sentence adds value, with no redundant information. The front-loaded overview immediately communicates the tool's purpose.
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 (6 parameters, hybrid search functionality) and the presence of an output schema, the description provides excellent context. It covers database scope, search methodology, parameter semantics, usage examples, and return format overview, making it complete enough for effective use without needing to explain return values in detail.
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?
With 0% schema description coverage, the description fully compensates by providing detailed semantic explanations for all 6 parameters. It explains query usage with examples, lists diet options, clarifies max_minutes filtering, defines difficulty values, explains servings parameter nuance, and specifies limit defaults and constraints.
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 searches a database of recipes using hybrid semantic search with reranking. It specifies the resource (50,000 recipes from Food.com) and distinguishes from the only sibling (ping) by describing a specific search functionality rather than a connectivity check.
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 clear context for when to use this tool (searching recipes with natural language queries) and includes multiple helpful examples. However, it doesn't explicitly state when NOT to use it or mention alternatives, though the only sibling tool (ping) serves a completely different purpose.
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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