get_job_categoriesRetrieve all job categories to populate and filter jobs and estimates with the correct job type.
List all available job categories (e.g., 'Commercial Service', 'Residential Install'). Use these values when creating jobs/estimates or filtering by category. Cached 30 minutes.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only, idempotent, and non-destructive. The description adds an important behavioral detail: the data is cached for 30 minutes, implying it may not reflect immediate changes. This goes beyond the annotations and helps the agent understand data freshness expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded. The first sentence identifies the action and resource, the second gives examples and use cases, and the third adds caching behavior. Every sentence contributes meaningful information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only lookup tool, the description covers what it returns, how it should be used, and the caching caveat. There is no output schema, but the examples sufficiently convey the expected return format. No critical information is missing for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so the description does not need to explain parameter semantics. With zero parameters, the baseline is 4, and the description appropriately provides examples of return values rather than parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that this tool lists all available job categories, which is a specific verb-resource combination. It provides concrete examples ('Commercial Service', 'Residential Install') and contrasts with other value-list siblings like get_job_statuses or get_sources. An agent can immediately recognize what this tool returns and how it differs from similarly named tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says to use these values when creating jobs/estimates or filtering by category, giving clear usage context. It does not explicitly name alternative tools or state when not to use it, but the guidance is specific enough that an agent will know when to call this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.