Skip to main content
Glama
Austinnui

vinance-recipes

by Austinnui

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct role: listing all recipes, searching across content, and fetching a specific recipe by ID. No overlap or ambiguity exists between them.

    Naming Consistency5/5

    All tool names follow the same verb+noun pattern in camelCase: listRecipes, searchRecipes, getRecipe. The style is uniform and predictable.

    Tool Count5/5

    Three tools form a tight, well-scoped set for a recipe server. Each tool addresses a different need without excess or deficiency.

    Completeness5/5

    The server covers the full read-side lifecycle for recipes: discovery (list/search) and retrieval (get). Since recipes are static content, no create/update/delete operations are necessary.

  • Average 4.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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.

  • Add related servers to improve discoverability.

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, the description carries the full burden. It adds useful behavioral details like 'copy-paste-ready implementation' and 'Pro recipes are shown with a 🔒 badge', but it does not disclose ordering, pagination, authentication needs, or how the 'all' category behaves. This is minimal but adequate for a simple read/list tool.

    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 with the main action front-loaded. Every sentence adds value, and there is no redundancy or fluff.

    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 tool is simple (one optional parameter, no output schema), and the description provides enough context: what recipes are, category options, and badge behavior. No return-value details are necessary for a list tool of this scope, and sibling context confirms its role.

    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 baseline is 3. The description lists the category values but omits the 'all' option, and it does not add meaning beyond what the schema's 'Filter by category. Default all' already provides. Marginal value; no contradictions.

    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 uses a specific verb ('List') with a clear resource ('production-ready React/Next.js code recipes') and provides category details and badge behavior. This clearly distinguishes it from the sibling tools 'searchRecipes' (search) and 'getRecipe' (get a single recipe).

    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 use for browsing all recipes and lists categories, but it does not explicitly explain when to use listRecipes versus searchRecipes or getRecipe. No exclusions or alternative guidance is provided, so usage context is only implicit.

    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?

    Since no annotations exist, the description carries the full transparency burden. It reveals that the search covers full source code, not just metadata, which is a valuable behavioral trait beyond what the schema specifies. It also implicitly frames the operation as a read-only search, though it does not disclose result format or potential limitations. This is adequate for a simple search tool.

    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, front-loaded with the primary action and scope, followed by useful topic examples. Every word earns its place, with no redundancy or filler.

    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 simplicity (one required parameter, no output schema), the description covers the search scope and provides varied examples. It does not mention what the return value looks like, which is a minor gap due to the absent output schema, but the name 'searchRecipes' strongly implies a list of recipes. Overall, the description is sufficiently complete for a lightweight search tool.

    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% and the parameter already includes a description with examples. The tool description adds context that the query applies across multiple fields (titles, descriptions, tags, source code), but this is more about behavior than parameter semantics. It does not add meaningful syntax, constraints, or format details beyond the schema.

    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 specifies a clear action (search) and a distinct resource (recipes) with explicit scope: keyword across titles, descriptions, tags, and full source code. This differentiates it from the sibling tools listRecipes (list all) and getRecipe (retrieve by ID). The verb-to-resource relationship is specific and unambiguous.

    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 strongly implies when to use it ('search by keyword', 'works for any topic') and provides common topic examples. However, it does not explicitly state when to use this tool instead of listRecipes or getRecipe, nor does it mention any exclusions or prerequisites. Clear context, but no direct alternative naming.

    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. It discloses the return format (copy-paste-ready code, usage examples, props reference, architecture notes), mentions possible backend patterns, and states that pro recipes require an API key. It lacks explicit discussion of errors or side effects, but is otherwise thorough.

    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 only two sentences, front-loaded with the core purpose, then elaborates on the return content. Every sentence adds value, with no redundancy or filler.

    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 absence of an output schema, the description explains what the tool returns in useful detail, including code, documentation, and auth requirements. It is complete enough for an agent to know what to expect from the tool.

    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% with descriptions for both id and apiKey. The description adds some context about pro recipes requiring the API key, but otherwise it does not add meaningful meaning beyond what the schema already provides. Baseline of 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 uses the specific verb 'Get' with the resource 'recipe by ID', clearly distinguishing it from listRecipes and searchRecipes. It immediately states what the tool does without ambiguity.

    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 clearly implies usage for retrieving a specific recipe by ID, which differentiates from sibling list/search tools. However, it does not explicitly mention when not to use it or name alternatives, so it misses the top score.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

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

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

vinance-recipes-skills MCP server

Copy to your README.md:

Score Badge

vinance-recipes-skills MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Austinnui/vinance-recipes-skills'

If you have feedback or need assistance with the MCP directory API, please join our Discord server