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DhanyaHegdek

LeaveManager

by DhanyaHegdek
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

50%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool clearly targets a distinct function: checking balance, applying for leave, and viewing history. No overlap or ambiguity.

    Naming Consistency5/5

    All tools follow a consistent 'verb_leave_noun' pattern (get_leave_balance, apply_leave, get_leave_history), making the set predictable.

    Tool Count4/5

    Three tools is minimal but reasonable for a basic leave manager. A few more (e.g., cancel or update leave) could be helpful, but the count is not inappropriate.

    Completeness2/5

    The set covers basic read and create operations but lacks essential operations like cancel, update, or approve leave, leaving significant gaps for a full leave management workflow.

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

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

    • No community issues in the last 6 months
    • 2 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

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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?

    With no annotations, the description must fully disclose behavioral traits. It does not mention that this is a read operation, nor does it describe the output structure, pagination, or scope of 'leave history'.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is very short (one sentence) and front-loaded, but it sacrifices essential detail for brevity. It is not overly verbose, but it is under-specified.

    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?

    Despite the presence of an output schema, the description lacks contextual completeness. It fails to clarify the input parameter, the difference from sibling tools, or the behavior of the tool.

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

    Parameters2/5

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

    The schema has 0% description coverage for employee_id, and the description only implies its use without adding meaning. It does not explain what employee_id represents or how to obtain it.

    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 action 'Get leave history' and the resource 'the employee', but it does not distinguish from sibling tools like get_leave_balance or apply_leave. It is not a tautology, but specificity is limited.

    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?

    No guidance is provided on when to use this tool versus alternatives such as get_leave_balance. There is no mention of prerequisites, context, or exclusions.

    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 exist, and the description covers only the action; it misses behavioral traits like side effects, authorization needs, or result handling expected from a mutation tool.

    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?

    Single sentence is concise and front-loaded, but the structure is minimal; could include more detail without bloat.

    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?

    For a 2-parameter tool with output schema, the description is too sparse: no return info, no lifecycle or effect beyond the example.

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

    Parameters2/5

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

    With 0% schema coverage, the description must explain parameters; it provides an example for leave_dates but omits any explanation for employee_id.

    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's action ('apply leave') and the resource ('specific dates'), distinguishing it from sibling tools like get_leave_balance and get_leave_history.

    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 purpose implies usage for applying leave, but the description does not explicitly contrast with alternatives or provide conditions for use vs. not use.

    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 convey behavioral traits. It only implies a read-only operation ('check') but omits details like permissions, side effects (e.g., whether the operation is logged), or the format of the result. With output schema existing, the return format is covered, but the description itself adds minimal 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 a single, front-loaded sentence of 8 words. Every word contributes to the purpose, and there is no redundancy or filler. Ideal conciseness for a simple tool.

    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 low complexity (one parameter, output schema exists), the description is functionally adequate but lacks context such as what specific leave-related data is returned (e.g., total, used, or remaining days) and when to prefer this over siblings. Output schema may fill some gaps, but the description alone leaves room for interpretation.

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

    Parameters2/5

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

    The only parameter 'employee_id' has no schema description (0% coverage) and the tool description merely says 'for the employee', offering no additional semantics about expected format, domain, or validation. The description does not compensate for the missing parameter documentation.

    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 action ('Check') and the resource ('how many leave days are left for the employee'), making the tool's purpose unambiguous. It also naturally distinguishes from sibling tools 'apply_leave' (applies leave) and 'get_leave_history' (retrieves history) by focusing on balance.

    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?

    No explicit guidance is given on when to use this tool versus its siblings. The description implies checking current balance but does not explain that 'get_leave_history' is for past usage or that 'apply_leave' is for deductions. This leaves the agent without decision criteria.

    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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