Look up remote job location or industry taxonomy values when a filter slug is uncertain, returning JSON for job search filters.
Legacy-compatible location or industry lookup. Returns the original JSON in content.text. Call when a filter slug is uncertain; obvious values can go directly to search_jobs.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v2.0.0Input schema / additionalPropertiesAdded value: +falseInput schema / properties / type / descriptionRemoved value: -"Taxonomy type to fetch."Output schema / (root)Previous value: -nullNew value: +{
+ "additionalProperties": true,
+ "type": "object"
+}v0.1.5Input schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"Input schema / additionalPropertiesRemoved value: -falsev0.1.4Input schema / properties / type / descriptionAdded value: +"Taxonomy type to fetch."v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered without the description. The description adds real context by noting it is legacy-compatible and where the payload lands ('original JSON in content.text'), though it omits auth requirements, rate limits and whether the returned set is paginated.
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?
Three short sentences with the purpose front-loaded before the usage condition and the return-location detail. Slightly awkward phrasing around 'original JSON in content.text', but no sentence is wasted.
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 single-parameter lookup with an output schema, the core need is met: what it returns, how to get it, and when to call it. The missing piece is disambiguation from resolve_taxonomy/list_taxonomies, which an agent has no way to resolve from the description alone.
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?
Only one parameter exists and it is a two-value enum whose values (locations/industries) are fully self-describing. The description reinforces the split by framing the tool as a 'location or industry lookup', so semantic coverage is effectively complete despite 0% schema description coverage.
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 gives a clear verb-implied resource ('location or industry lookup') and matches the enum values in the schema, so an agent knows exactly what data comes back. It differentiates itself from search_jobs, but says nothing about how it differs from its closest siblings, resolve_taxonomy and list_taxonomies, which is the overlap that matters most.
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?
There is an explicit when-to-use condition ('when a filter slug is uncertain') paired with a when-not/alternative ('obvious values can go directly to search_jobs'), which is more than most definitions offer. The guidance is undercut by not addressing resolve_taxonomy or list_taxonomies, the siblings an agent is most likely to confuse it with.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.