Leave Manager MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation4/5
The tools are mostly distinct: balance, history, types, apply, cancel, and employee details each target a clear purpose. The only mild overlap is between leave balance and leave history, but their intent is sufficiently separated.
Naming Consistency4/5Most tools follow a get_/apply_/cancel_ pattern with snake_case. The outlier is get_employee_details_by_employeeId which mixes an 'employeeId' camelCase segment into an otherwise snake_case name, causing a minor inconsistency.
Tool Count5/5Six tools is well-scoped for a leave management server. Each tool covers an essential function without unnecessary bloat or significant redundancy.
Completeness3/5The core employee self-service workflow is covered: view balance, history, types, apply, and cancel. Missing tools for approval/rejection or checking pending leave requests create notable gaps for a 'manager' context.
Average 2.9/5 across 6 of 6 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?
No annotations are provided, so the description carries the full burden. It does not disclose any behavioral traits such as whether it is read-only, side effects, authentication requirements, or error handling. For a get operation, it implies read-only but does not state it, and no output schema is provided, leaving behavior largely opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (5 words), which could be considered concise, but it under-specifies rather than being efficiently informative. It is front-loaded with the purpose, but the brevity results in missing critical information, making it insufficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one parameter, no output schema, and no annotations. Given this, the description should at least indicate what 'employee details' entails (e.g., which fields are returned) and any special cases. It does not, so it is incomplete for practical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%: the parameter employeeId has no description in the schema. The tool description repeats 'by employeeId' but adds no additional meaning. Given low coverage, the description should compensate but does not clarify format (e.g., UUID, string) or any constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states it retrieves employee details by employeeId, which is a clear verb+resource+identifier. However, it lacks differentiation from sibling tools (e.g., get_leave_balance, apply_leave) which are about leave, not employee details, so there is some implicit distinction. It is not a tautology but is minimal.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not specify when to use this tool versus alternatives, but since it is the only employee details tool among leave-focused siblings, the usage context is somewhat implied. Still, no explicit guidance for when-not-to-use or prerequisites.
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 at all, so the description carries full burden. It only says 'get' implying a read, but does not disclose return format, whether it includes only approved leaves, date ranges, or any limits. It gives no behavioral details beyond the verb.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short, one line, and front-loaded with the purpose. It is concise, but it is under-specified rather than efficiently concise. Since it avoids fluff, it at least earns a baseline for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema and no annotations, the description is insufficient. It does not explain what 'history' includes, whether there are any filters, pagination, or typical use cases. The complexity is moderate but the description is too thin to be complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for the single parameter employeeId. The description does not elaborate on what employeeId is or any format requirements. It merely repeats the parameter name implicitly. The description adds minimal value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Get the leave history of an employee' which is a clear verb+resource. It distinguishes from siblings like get_leave_balance (which implies current balance) and apply_leave, but does not explicitly differentiate what 'history' includes (e.g., past applications, approved leaves, status over time). It is acceptable but lacks specifics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. It doesn't mention that this is for historical records, nor does it contrast with get_leave_balance for current entitlements. The agent must infer usage from the name alone.
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 burden of explaining behavior. It only states that leave is applied for, but does not disclose side effects, potential validations, approval implications, or return behavior. This is insufficient for 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The one-line description is clear and front-loaded, with no filler or redundant phrasing. It is short and easy to parse, though its brevity sacrifices important operational details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a 5-required-parameter mutation tool with no annotations, output schema, or parameter explanations, the description is not sufficient to support correct invocation. It captures the core action but leaves critical operational context undocumented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and no mention of parameters in the description, the agent receives no additional semantic meaning. The schema provides names and types, but nothing explains date formats, reason expectations, or how the employeeId is resolved.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Apply for leave for an employee' uses a specific verb and resource, clearly communicating what the tool does. It also distinguishes itself from the sibling read and cancel tools by indicating the creation/submission action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no explicit guidance on when to use this tool versus the sibling tools, nor does it mention prerequisites or exclusions. Usage can only be inferred from the action itself, making this a weak dimension.
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 provided, so description must carry full burden. 'Cancel' implies a mutation, but it doesn't disclose side effects (e.g., whether the cancellation is irreversible, whether it requires special permissions, how it affects leave balance). Does not contradict any annotations since none exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, perfectly concise. No fluff, front-loaded action. It serves the purpose with minimal words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a mutation tool with no annotations and no output schema, the description is lacking. It doesn't mention expected outcome, error conditions, or anything about the cancellation process. For a simple tool with one param, it is minimal but functional, yet incomplete in providing useful context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, meaning the description doesn't explain the leaveId parameter beyond its name from the schema. The description simply says 'an existing leave request' without adding meaning like what the ID format is or where to find it. With 0% coverage, the description must compensate, but it does not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb+resource: 'Cancel an existing leave request.' It specifies the action and object, and while it doesn't explicitly differentiate from siblings, the sibling tools like apply_leave and get_leave_history are distinct. It is direct 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 Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. It doesn't mention any conditions for cancellation, such as approval status or time limitations. The context is implied but not stated, so no exclusions or alternative references.
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 the full burden of behavioral disclosure. It only states the action without explaining what the tool returns, whether it requires specific permissions, or any side effects. For a read operation, it doesn't mention the output format or any limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that is front-loaded with the main action. It is appropriately brief, though it could add a bit more detail without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description is minimal but lacks important context such as what the balance includes (e.g., annual, sick, etc.) or any time-based considerations. It is adequate for a basic read but incomplete for an agent to fully understand the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, and the description doesn't explain the 'employeeId' parameter beyond its name. However, with only one parameter and a clear name, the meaning is fairly obvious. The description adds no extra semantic value, but the parameter is self-explanatory.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to retrieve the current leave balance for an employee. It uses a specific verb ('get') and resource ('leave balance'), and it is distinct from sibling tools like get_leave_history and get_leave_types, though it doesn't explicitly differentiate itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions. The sibling tools suggest related but different functions, but the description doesn't clarify when to choose this one.
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 disclose behavioral traits. It only states the action without mentioning side effects, authentication requirements, or whether it is a read-only operation. For a simple list tool, the lack of such disclosure is a gap, though the risk is low.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single, clear sentence that conveys the entire purpose without any filler or unnecessary details. Perfectly concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema, no nested objects), the description is adequate. It covers the core purpose and does not leave critical gaps, though it could mention the return format or any filtering options for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema provides all necessary context. Per the baseline rule, a score of 4 is appropriate; the description adds no parameter-specific information, but none is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get all available leave types' uses a specific verb ('Get') and resource ('leave types'), and clearly distinguishes from sibling tools like get_leave_balance or get_leave_history which deal with specific aspects of leave. It is unambiguous about what it returns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states what the tool does but provides no guidance on when to use it versus alternatives. However, since the tool is a simple list operation with a unique purpose, the intended usage is easily inferred, though not explicit.
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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