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Para-FR

satelia-mcp-lucca

by Para-FR
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

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 entity: absences, collaborators, and departments. There is no overlap as they handle different data types, making disambiguation straightforward for an agent.

    Naming Consistency5/5

    All tools follow a consistent 'lister-' verb-noun pattern in French, with clear and predictable naming. No mixing of conventions.

    Tool Count4/5

    With only 3 tools, the server is focused and uncluttered. The count is slightly below typical ranges but appropriate for a minimal read-only HR data access server.

    Completeness3/5

    The set covers listing of three core entities, but lacks write operations (create, update, delete) and more advanced filters. For a read-only server it's acceptable, but there are notable gaps in full lifecycle coverage.

  • Average 3.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 11 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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  ]
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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

  • 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. It explains that the tool lists and searches collaborators, and that the ID is useful for absences. However, it does not disclose the return format (beyond ID), pagination behavior, or ordering. For a simple tool, this is acceptable but not exhaustive.

    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 very concise: two sentences that efficiently cover purpose and a specific use case. Every sentence is necessary and adds value without 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?

    Given the tool's simplicity (3 optional parameters, no output schema), the description is mostly complete. It explains the main function and a key use case. However, it lacks explicit mention of the return structure (e.g., which fields are returned besides id) and pagination details, leaving some gaps for a fully self-contained description.

    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 input schema has 100% description coverage, so the baseline is 3. The description adds minimal extra meaning beyond the schema: it reiterates the search fields (prenom, nom, email) and the utility of the ID. It does not provide new parameter semantics beyond what the schema already offers.

    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 it lists collaborators (salaries) of the Lucca instance and allows searching by first name, last name, or email to retrieve an ID. This distinguishes it from sibling tools like lister-absences and lister-departements, which handle different resources.

    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 used to find a collaborator's ID for later consulting absences, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. There is no mention of when not to use it or what to do if results are empty.

    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 carries full burden. It does not disclose any behavioral traits such as read-only nature, destructive potential, authorization requirements, or side effects. The description only states the function without additional 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 sentences, front-loaded with the main action, and contains no unnecessary words. Every sentence adds value.

    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 description explains what is returned (id and code) and provides a use case. Given the tool's simplicity (single optional parameter, no output schema), it is mostly complete. Missing details like pagination or default behavior, but these are not critical for this 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 one parameter. The description does not add meaning beyond the schema's description for 'actifsSeulement'. Baseline 3 is appropriate as the schema already documents the parameter.

    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 lists departments with their ID and code, and specifies the organization (Lucca). It differentiates from siblings by mentioning it's useful for filtering absences, which implies distinct function from lister-absences and lister-collaborateurs.

    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 provides a specific use case (filtering absences by department), giving context for when to use this tool. However, it does not explicitly state when not to use it or compare with siblings beyond the mention.

    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 provided, so description carries full burden. It discloses that it lists absences (read operation) and that each line is a half-day. Lacks details on authentication or limits beyond the limit parameter.

    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?

    Very concise two sentences, front-loaded with main purpose, no unnecessary words.

    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 no output schema, description explains output format (each line is a half-day) and date format. Includes limit parameter details. Could improve by mentioning default sorting or that the two ID parameters are mutually exclusive.

    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?

    Schema coverage is 100% with good descriptions, and the description adds value by clarifying the exclusive use of collaborateurId or departementId and linking to sibling tools for ID lookup. Cannot score 5 because description does not explicitly state that the parameters are mutually exclusive.

    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 it lists absences over a period for one employee or one department, distinguishing it from sibling tools that list employees or departments.

    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?

    Specifies that the tool can be used with either employee ID or department ID, and mentions how to find those IDs using sibling tools. However, does not explicitly state when not to use it.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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