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fetch_employee_paystatement_details

Retrieve detailed pay statement information for a specific employee, year, and check date from the Paylocity system.

Instructions

Fetch pay statement details for a specific employee, year and check date.

Args: company_id: Optional company ID (string or integer). If not provided, uses the first company ID from configuration. employee_id: Employee ID (string or integer) to get pay statement details for. year: The year to get pay statement details for. check_date: The check date to get pay statement details for (format: MM/DD/YYYY).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_idNo
employee_idNo
yearNo
check_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'fetch_employee_paystatement_details' tool. It is decorated with @mcp.tool() which registers it, includes input schema via type hints and docstring, validates parameters, and delegates to the Paylocity client.
    @mcp.tool()
    def fetch_employee_paystatement_details(
        company_id: Optional[Union[str, int]] = None, 
        employee_id: Union[str, int] = None,
        year: Union[str, int] = None,
        check_date: str = None
    ) -> Dict[str, Any]:
        """
        Fetch pay statement details for a specific employee, year and check date.
        
        Args:
            company_id: Optional company ID (string or integer). If not provided, uses the first company ID from configuration.
            employee_id: Employee ID (string or integer) to get pay statement details for.
            year: The year to get pay statement details for.
            check_date: The check date to get pay statement details for (format: MM/DD/YYYY).
        """
        if any(param is None for param in [employee_id, year, check_date]):
            raise ValueError("employee_id, year, and check_date are all required")
            
        company_id_str = str(company_id) if company_id is not None else company_ids[0]
        employee_id_str = str(employee_id)
        year_str = str(year)
        return client.get_employee_paystatement_details(company_id_str, employee_id_str, year_str, check_date)
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 for behavioral disclosure. While 'Fetch' implies a read-only operation, the description doesn't address important behavioral aspects like authentication requirements, rate limits, error conditions, or what happens when parameters are invalid. It mentions a default behavior for company_id but provides minimal operational context.

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?

The description is efficiently structured with a clear purpose statement followed by a parameter-by-parameter explanation. Each sentence serves a specific purpose without redundancy. The formatting with 'Args:' section makes it easy to scan, though the parameter explanations could be slightly more detailed given the lack of schema descriptions.

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 that there's an output schema (which handles return values), 0% schema description coverage, and no annotations, the description does an adequate job covering the basic purpose and parameters. However, for a tool that likely accesses sensitive payroll data, it should provide more context about authentication, data sensitivity, and error handling to be truly complete.

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?

With 0% schema description coverage, the description provides essential semantic context for all 4 parameters that the schema lacks. It explains that company_id is optional with a default behavior, identifies employee_id as the target employee, clarifies year as the period for pay statements, and specifies the exact date format for check_date. This significantly compensates for the schema's lack of descriptions.

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 ('Fetch pay statement details') and target resource ('for a specific employee, year and check date'), making the purpose unambiguous. However, it doesn't explicitly differentiate this tool from sibling tools like fetch_employee_earnings or fetch_employee_details, which likely retrieve different types of employee financial data.

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?

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like fetch_employee_earnings and fetch_employee_details available, there's no indication of what distinguishes this pay statement details tool from those other employee data retrieval tools.

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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