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fetch_employee_local_taxes

Retrieve local tax information for a specific employee from Paylocity payroll data. Use this tool to access tax details by providing employee and company identifiers.

Instructions

Fetch local taxes for a specific employee.

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 local taxes for.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_idNo
employee_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for fetch_employee_local_taxes that validates input and calls PaylocityClient method
    @mcp.tool()
    def fetch_employee_local_taxes(company_id: Optional[Union[str, int]] = None, employee_id: Union[str, int] = None) -> Dict[str, Any]:
        """
        Fetch local taxes for a specific employee.
        
        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 local taxes for.
        """
        if employee_id is None:
            raise ValueError("employee_id is required")
            
        company_id_str = str(company_id) if company_id is not None else company_ids[0]
        employee_id_str = str(employee_id)
        return client.get_employee_local_taxes(company_id_str, employee_id_str)
  • PaylocityClient method implementing the API call to fetch employee local taxes using the /localTaxes endpoint with authentication and retry logic handled by _make_request
    def get_employee_local_taxes(self, company_id, employee_id):
        """Get all local taxes for a specific employee"""
        endpoint = "/api/v2/companies/{}/{}/localTaxes".format(company_id, employee_id)
        return self._make_request("GET", endpoint).json()
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'fetches' data (implying read-only), but doesn't mention authentication requirements, rate limits, error conditions, or what the output contains. For a tool that accesses potentially sensitive employee tax data, this lack of behavioral context is a significant gap, though not contradictory.

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 perfectly structured and concise. The first sentence states the core purpose clearly. The Args section efficiently documents parameters with just enough context. Every sentence earns its place with no wasted words, and information is front-loaded appropriately.

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), no annotations, and only 2 parameters with 0% schema coverage, the description is moderately complete. It covers the basic purpose and parameter semantics adequately but lacks behavioral context and usage guidelines. For a tool accessing sensitive employee data, more completeness would be expected, but the output schema reduces the burden somewhat.

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 description coverage is 0%, so the description must compensate. It adds meaningful context for both parameters: company_id is optional with a default behavior (uses first company from config), and employee_id specifies it's 'to get local taxes for'. This clarifies purpose beyond the schema's type definitions. However, it doesn't explain format constraints or provide examples, keeping it from a perfect score.

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 verb 'fetch' and the resource 'local taxes for a specific employee', making the purpose immediately understandable. It distinguishes from siblings like fetch_employee_details or fetch_employee_earnings by specifying the exact data type (local taxes). However, it doesn't explicitly differentiate from all siblings (e.g., fetch_employee_paystatement_details might also contain tax info), so it's not a perfect 5.

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. It doesn't mention when to choose this over fetch_employee_details (which might include tax info) or fetch_employee_paystatement_details, nor does it specify prerequisites or context for usage. The only implicit guidance is that it's for local taxes specifically, but no explicit alternatives or exclusions are stated.

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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