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mcp-secure-server-giulia-ai

by wganalytics
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Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct function: one validates the API key and authenticates the user, the other lists all users. There is no overlap or ambiguity between them.

    Naming Consistency2/5

    The tool names are inconsistent: 'tool_segura' uses a vague 'tool_' prefix with a non-verbal name, while 'listar_usuarios' follows a clear verb_noun pattern. This mixed convention makes the set feel uncoordinated.

    Tool Count3/5

    Two tools is at the low end of the acceptable range. It is understandable for a limited server, but the small count feels thin relative to the 'secure server' positioning.

    Completeness2/5

    The server covers authentication validation and listing users, but lacks user management operations such as get by ID, create, update, or delete. This creates significant gaps in the implied user management domain.

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

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

    • No community issues in the last 6 months
    • 3 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.

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  • 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?

    No annotations are present, so the description carries the full burden. It discloses that the tool validates an API key and returns a greeting, implying a read-only/validation operation, but does not mention error handling, side effects, or how invalid keys are treated. This is adequate but not 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?

    A single, front-loaded sentence with no redundant information; every word contributes to the purpose.

    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 zero-parameter tool with an output schema, the description covers the core behavior and the output is documented elsewhere. It lacks explicit error-handling or authentication-header details, but these are minor given the tool's simplicity.

    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 tool has zero parameters and the input schema is empty (100% coverage). The description adds context that an API key is validated, which clarifies why no parameters are needed. Baseline is 4 for zero-parameter tools.

    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 states a specific verb ('Valida a API Key') and a clear outcome ('retorna uma saudação com os dados do usuário autenticado'), which distinguishes it from the sibling 'listar_usuarios' that lists users. An agent can understand exactly what the tool does.

    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 context (validate API key and get authenticated user's greeting) but does not explicitly contrast with 'listar_usuarios' or state when to choose one over the other. There is no exclusion or alternative guidance.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose that the tool returns name and email for each user, making it clearly a read-only listing operation. But it does not mention authentication needs, pagination behavior, ordering, or possible failure modes, which would strengthen 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 short sentences with no filler. The primary action is front-loaded, and the return fields are stated immediately. Every sentence earns its place.

    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 zero-parameter, simple listing tool, the description is complete: it states what is listed and what is returned. The presence of an output schema further covers return details, and there are no required parameters or nested objects that need explanation.

    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 zero properties, and the description correctly adds no confusing parameter information. With 0 parameters, the baseline is 4 because there is nothing for the description to compensate for. The description appropriately focuses on the operation and output.

    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 and resource: 'Lista todos os usuários cadastrados no sistema.' It clearly states the tool's scope (all registered users) and tells the agent what data is returned (name and email). This is unambiguous and distinguishes the tool by domain from its sibling tool_segura.

    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 should be used when you need all registered users and their names/emails. However, it does not explicitly state when not to use it, nor does it mention tool_segura or any alternative conditions. The guidance is adequate but left mostly to inference.

    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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