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laizaguedes

receitas-mcp-server

by laizaguedes

Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clear and distinct purpose: listing, fetching details, adding, removing, and suggesting recipes. The overlap between listar and buscar is resolved by list returning a filtered collection while buscar returns a single full recipe.

    Naming Consistency5/5

    All tool names follow a consistent Portuguese verb_noun pattern: listar_receitas, buscar_receita, adicionar_receita, remover_receita, and sugerir_por_ingredientes. The pattern is uniform and predictable.

    Tool Count5/5

    With 5 tools, the server is well-scoped for a recipe management domain. Each tool covers a core operation without unnecessary redundancy.

    Completeness4/5

    The server covers list, get, create, and delete operations, but lacks an update tool for editing existing recipes. This is a minor gap that agents can work around by deleting and recreating, but it is not a fatal omission.

  • Average 3.7/5 across 5 of 5 tools scored. Lowest: 3.1/5.

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

    • No community issues in the last 6 months
    • 1 commit 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
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  • This repository includes a README.md file.

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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

  • Behavior2/5

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

    No annotations are provided, so the description carries full behavioral disclosure burden. It only states the basic listing action and filter availability, but does not disclose read-only nature, permissions, pagination, limits, return format, or any other behavioral traits beyond the visible action.

    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 a single, efficient sentence that front-loads the primary action and lists optional filters. No unnecessary words or redundancy.

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

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool is simple and the description covers the core listing and filter capability. However, given the absence of annotations and output schema, the description should provide more context about expected output, usage boundaries, or alternatives to be fully complete. It is adequate but has clear gaps.

    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 baseline is 3. The description merely restates the filter dimensions (category, difficulty, ingredient, free search) without adding new meaning or clarifying syntax, format, or usage nuances beyond what the schema already provides.

    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 verb 'Lista' (lits) and the resource 'receitas' (recipes), indicating a listing operation with optional filters. It distinguishes from most siblings like 'adicionar_receita' and 'remover_receita', but does not explicitly differentiate from 'buscar_receita' given the 'busca livre' filter, which could overlap in purpose.

    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?

    The description gives no guidance on when to use this tool versus alternatives like 'buscar_receita' or 'sugerir_por_ingredientes'. It only mentions optional filters without explaining scenarios where one tool would be preferred over another.

    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 discloses that data is persisted to data/receitas.json and that the operation fails if the id already exists, which are important side-effect and error conditions. However, it does not mention success behavior, return values, or any permissions/authorization requirements.

    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 extremely concise, consisting of two short sentences. The primary action verb 'Cadastra' appears first, and the additional sentence efficiently communicates the key failure condition. There is no redundant or filler content.

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

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool is a creation operation with 12 parameters, no annotations, and no output schema, yet the description provides only the core function and one error condition. It fails to convey essential context such as required vs. optional fields, default values for optional parameters, array structure for ingredientes/modoPreparo, or what happens on success. This is insufficient for a tool of this complexity.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema has 0% description coverage, and the description adds no parameter-level semantics. It does not mention any of the 12 parameters, their formats, defaults, or relationships. The parameter names (e.g., titulo, ingredientes) are somewhat self-explanatory for a recipe domain, but the description itself provides no clarification or additional context 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 begins with 'Cadastra uma nova receita' (Registers a new recipe), which is a specific verb+resource pair that clearly identifies the tool's function. It also distinguishes it from sibling tools like listar_receitas, buscar_receita, remover_receita, and sugerir_por_ingredientes, ensuring no ambiguity.

    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 when a new recipe needs to be added, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. The note that it fails if the id already exists offers some situational guidance, but no alternative tools are mentioned, so guidance remains implicit.

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

  • Behavior2/5

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

    No annotations are provided, so the description must carry the behavioral disclosure burden. It only states 'Remove' without revealing whether the deletion is permanent, requires permissions, or affects related data. For a destructive operation, this is insufficient.

    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 a single, clear sentence that front-loads the action and the required input. It contains no redundant words or vague phrasing.

    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?

    The tool is simple with one required parameter and no output schema. The description adequately conveys the core operation, but it lacks any mention of success/failure behavior or irreversible consequences, which would be useful for completeness.

    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?

    The schema already provides full coverage of the single parameter 'id' with a description ('id da receita a remover'). The tool description adds no additional semantic meaning, so the baseline 3 applies.

    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 ('Remove') and resource ('receita') with the required identifier ('pelo id'). This clearly distinguishes it from sibling tools like 'listar_receitas' and 'adicionar_receita'.

    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 the tool is for deleting a recipe by id, but it does not explicitly state when to use it versus alternatives or provide exclusions. There is no mention of prerequisites like having a valid id or when not to use this tool.

    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?

    No annotations are provided, so the description carries the burden. It states that the tool 'returns all details', which conveys a read-only operation and the scope of the response. It does not disclose error behavior for missing ids or the exact return structure, but for a simple retrieval this is reasonably transparent.

    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 a single concise sentence that front-loads the purpose ('Retorna todos os detalhes') and the target resource. 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?

    For a simple tool with one parameter and no output schema, the description sufficiently covers purpose and usage. The phrase 'todos os detalhes' gives an overview of return value, though it could be more specific about the response structure or behavior when the id does not exist. Overall, adequate for the tool's complexity.

    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?

    The schema documentation covers 100% of the parameters (id) with a clear example ('brigadeiro'). The tool description merely echoes the schema by saying 'a partir do seu id', adding no extra semantic meaning. Baseline 3 is appropriate given full 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 uses the specific verb 'Retorna' (returns) and identifies the resource 'receita' (recipe) with its id, clearly distinguishing it from sibling tools like listar_receitas, adicionar_receita, remover_receita, and sugerir_por_ingredientes. This is a focused single-recipe-by-id retrieval.

    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 implies clear usage: when you have a recipe id and need all its details. It does not explicitly contrast with listar_receitas or provide exclusions, but the context is unambiguous from the title and description.

    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, the description carries the full burden. It discloses the ordering logic and the fact that missing ingredients are shown. It does not state whether the operation is read-only, but the verb 'sugere' implies no side effects and no mutation. Adds meaningful behavioral context beyond the basic action.

    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 a single, concise sentence that front-loads the input condition and states the action and output. Every element earns its place, with no redundancy.

    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?

    For a simple one-parameter tool without an output schema, the description adequately explains input and output behavior. It could be more explicit about whether zero-match recipes are included and what fields are returned, but it is largely complete for typical use.

    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?

    The schema already describes the parameter as 'Ingredientes que voce tem em casa' with type array of strings, so coverage is 100%. The description repeats the notion of available ingredients but adds no new parameter-level details, meriting the baseline 3.

    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 action ('sugere receitas') and the input (list of available ingredients), and describes the sorting behavior by match count and display of missing ingredients. This distinguishes it from sibling tools like listar_receitas and adicionar_receita.

    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 implies the situation for use: when the user has a list of ingredients and wants recipe suggestions. It does not explicitly exclude other tools or name alternatives, which prevents a 5, but the context is clear enough for a 4.

    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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