kl_categoriesList top-level Karlancer categories with category_id values and province names to browse projects by category or find valid province filters for freelancers.
List Karlancer's top-level categories with the ids kl_projects takes as category_id, and the provinces.
Use this to browse by category instead of keyword, or to get valid province names for kl_freelancers.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent and non-destructive, so the safety profile is covered. The description adds genuine value beyond them by revealing that only top-level categories are returned (not a full tree) and that the output feeds two specific consumers, `category_id` on kl_projects and `province` on kl_freelancers. It stops short of describing the response shape, such as whether categories and provinces come back as separate lists.
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?
Two short sentences, no filler: the first names what is returned and its shape, the second routes the agent to the correct usage. The downstream consumer is named before the alternative use case, which is the right order.
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 tool with rich annotations, the description supplies what the agent needs: scope (top-level only), the meaning of the two outputs, and which siblings consume them. Without an output schema, one more detail (e.g. that categories and provinces are returned together as separate collections) would close the remaining gap.
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 tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline is a 4. The description correctly implies a parameterless enumeration rather than a filtered lookup.
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?
States a specific verb and resource ('List ... top-level categories ... and the provinces') and immediately ties the output to concrete downstream use (the ids kl_projects accepts as `category_id`). An agent can distinguish this from kl_skills or kl_services without opening any schema.
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?
Explicitly names two use cases: browsing by category instead of keyword, and obtaining valid `province` values for kl_freelancers. That is real when-to-use guidance with a named alternative (keyword search), though there is no explicit when-not-to-use or prerequisite context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.