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AIDataNordic

food-recipe-mcp

by AIDataNordic
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

83%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.2

  • Disambiguation5/5

    The two tools have completely distinct purposes: ping is a connectivity test with no domain functionality, while search_recipes is the core recipe search operation. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Both tools use snake_case naming, which is consistent. However, ping uses a generic network term while search_recipes follows a verb_noun pattern, creating a minor deviation in naming style.

    Tool Count2/5

    With only 2 tools, this server feels severely under-equipped for a recipe domain. A recipe server should typically include tools for retrieving recipe details, filtering by criteria, saving favorites, or managing collections, not just search and ping.

    Completeness2/5

    The server lacks basic CRUD operations for recipes. While search_recipes is well-described, there are no tools to get full recipe details, create/modify recipes, or manage user interactions. This creates significant gaps for agent workflows.

  • Average 4.1/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 0 of 2 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • View related servers

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It discloses that the tool returns a greeting to confirm server running, which is helpful behavioral context. However, it doesn't mention potential side effects, error conditions, or performance aspects like response time, leaving some gaps in transparency.

    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 two concise sentences that are front-loaded with the main purpose. Every sentence adds value: the first states what it does, and the second explains the outcome. There is no wasted verbiage, making it highly efficient.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    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) and the presence of an output schema, the description is reasonably complete. It explains the tool's purpose and expected behavior adequately, though it could benefit from mentioning the parameter's role or typical use cases for better context.

    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?

    The input schema has 1 parameter with 0% description coverage, so the description must compensate. It doesn't explicitly mention the 'name' parameter, but implies a greeting response that likely incorporates it. Since there's only one optional parameter with a default, the description's focus on the tool's purpose provides adequate context, though not detailed param semantics.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose as a 'connectivity test' that 'returns a greeting to confirm the server is running.' It specifies the verb ('test'), resource ('server connectivity'), and outcome ('greeting'), though it doesn't explicitly differentiate from the sibling 'search_recipes' tool, which serves a completely different function.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage for testing server connectivity, but doesn't provide explicit guidance on when to use it versus alternatives or any prerequisites. It suggests a context of verifying server status, which is useful but lacks detailed exclusions or comparisons to other tools.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the search method (hybrid semantic search with reranking), database characteristics, multilingual support with auto-translation, and the types of information included in recipes. It doesn't mention rate limits, authentication needs, or pagination behavior, but provides substantial operational context.

    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 appropriately sized and front-loaded with the core functionality. The first sentence establishes the purpose, followed by database context and usage guidance. The query examples are valuable but slightly lengthy. Overall, most sentences earn their place, though some trimming of examples might improve conciseness.

    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 (semantic search with multiple filters), the description provides comprehensive context about the database, search methodology, and usage patterns. With 100% schema coverage and an output schema present, the description doesn't need to explain parameters or return values. It effectively complements the structured data with operational guidance.

    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 description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds some context about the database content and query examples, but doesn't provide additional parameter semantics beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting.

    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 searches a recipe database using hybrid semantic search with reranking. It specifies the database size (~50,000 recipes), source (Food.com), and content coverage (cuisines, meal types, cooking styles, nutritional info, difficulty ratings, user ratings). This distinguishes it from the only sibling tool 'ping' and provides specific verb+resource details.

    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 guidance on when to use this tool: for searching recipes with natural language queries covering cuisine, style, main ingredient, occasion, or mood. It includes multiple concrete examples of appropriate queries and notes that queries in any language are supported. Since the only sibling is 'ping', the distinction is clear.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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