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Housecall Pro MCP Server and API by Cotlet

List employees

list_employees
Read-onlyIdempotent

The company's employees (field techs, office, admins) with role and contact, for dispatch. Use employee_id in create_job and create_estimate to assign work.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_employees"
  }
}

Input Schema

NameRequiredDescriptionDefault
include_archivedNoAlso archived employees. Default: false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and a closed-world scope, so safety is covered. The description adds useful context about what the records contain (role and contact) and their dispatch purpose, but says nothing about pagination, sorting, or how archived records are treated beyond what the schema field already states.

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?

Two compact sentences with no filler, and the scope statement is front-loaded ahead of the cross-reference to create_job/create_estimate. The opening sentence is a fragment rather than a complete clause, which slightly weakens the lead.

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?

For a simple, non-destructive single-parameter list tool with full annotation coverage and no output schema, the description conveys enough: what entities are returned, which fields matter, and where the IDs are consumed. Only return-shape and volume details are left implicit.

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 description coverage is 100% with a single optional parameter, so the schema already documents include_archived and its false default. The description adds no additional meaning about that parameter, making the baseline 3 appropriate.

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 names the resource (the company's employees) and enumerates sub-types (field techs, office, admins) plus the returned fields (role, contact), which clearly distinguishes it from list_customers or list_jobs. It stops short of an explicit verb phrasing like 'list/retrieve', so the operation is inferred from the name rather than stated.

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?

'for dispatch' and 'Use employee_id in create_job and create_estimate to assign work' give real downstream context and name sibling tools. However there is no guidance on when to call this versus the other list_* tools or any exclusion, so the when-to-use dimension is only implied.

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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