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HR-Partner

HR Partner MCP Server

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by HR-Partner

List Leave Balances

list_leave_balances

Retrieve employee leave balances for entitlement, current balance, carryover, units, and accrual details. Filter by employee or policy to reduce computation time.

Instructions

Get employee leave balances per leave policy: entitlement, current balance, carryover, units and accrual details. Warning: balances are slow to compute, so always filter to the smallest dataset needed (e.g. a single employee or policy).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
employeeNoEmployee code to only return records for that particular employee
locationNoOne or more location names (separated by commas) to filter by
positionNoOne or more position names (comma separated)
is_activeNoInclude active employees in the results (default true) (true/false)
departmentNoOne or more department names (separated by commas) to filter by
max_recordsNoOptional client-side limit on the number of records returned. Use to keep responses small when exploring data; the API itself does not paginate.
is_terminatedNoInclude terminated employees in the results (default true) (true/false)
absence_reasonNoLeave policy name to isolate balances against that policy
employment_statusNoOne or more employment statuses (comma separated)
Behavior4/5

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

There are no annotations, so the description carries the full burden. It discloses a significant performance trait ('balances are slow to compute') and implies read-only behavior through 'Get.' It also indicates the returned content, though it does not detail auth or side effects (which are not relevant for a read-only tool).

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?

Two sentences with zero fluff: the first defines the tool's scope, the second delivers crucial performance guidance. Every word contributes. It is appropriately front-loaded and easy to parse.

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 the tool's complexity (9 optional params, no output schema), the description adequately conveys what the tool returns and warns about computational cost. It doesn't explain pagination, but the max_records parameter description covers that. The enumeration of output fields compensates for the lack of an output schema.

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%, so the parameters are already well documented. The description references 'employee' and 'policy' as filter examples, aligning with the employee and absence_reason parameters, but adds no additional meaning beyond what the parameter descriptions already provide.

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 'Get' and clearly identifies the resource: 'employee leave balances per leave policy.' It enumerates the data fields (entitlement, current balance, carryover, units, accrual details), which distinguishes it from sibling tools like list_absences and list_leave_requests.

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 gives explicit usage guidance: 'always filter to the smallest dataset needed' with concrete examples (single employee or policy). It does not explicitly name alternatives or state when not to use this tool, but provides strong contextual guidance for effective invocation.

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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