Find employers by typing a partial company name. Returns matching suggestions to autocomplete your search.
Autocomplete company names. Returns matching employer suggestions for partial input.
| Name | Required | Description | Default |
|---|---|---|---|
| raw | No | Return the full raw hh.ru JSON instead of the compact id — name listing. | |
| text | Yes | Partial company name to autocomplete |
Changes observed during successful MCP inspections.
v2.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It states that matching employer suggestions are returned for partial input, which is a minimal behavioral trait. However, it does not disclose potential rate limits, result limits, authentication requirements, or the effect of the 'raw' parameter on output. Given the absence of annotations, this is insufficient for a production tool.
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 exceptionally concise with two sentences that convey the essential action and output. It is front-loaded with the verb 'autocomplete' and the object 'company names', with no redundant boilerplate. Every word earns its place.
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 simple autocomplete tool with two parameters and no output schema, the definition covers the core function but lacks usage guidance and deeper behavioral transparency. The description doesn't explain the response format beyond 'matching employer suggestions', nor does it clarify the difference between compact and raw output. It is adequate for basic invocation but incomplete for nuanced decision-making.
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?
Schema description coverage is 100% since both 'text' and 'raw' have descriptions in the schema. The tool description adds no parameter-specific information beyond what the schema already provides. Per the baseline for high schema coverage, a score of 3 is appropriate.
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 the tool's function: autocomplete company names and return employer suggestions for partial input. This distinguishes it from sibling tools like suggest_positions and suggest_areas which handle different entities. However, it doesn't explicitly differentiate from search_employers, which could also return employer matches, so it's not fully distinct.
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 phrase 'for partial input' implies this is meant for typeahead/autocomplete scenarios, but there is no explicit guidance on when to prefer this over suggest_positions, suggest_areas, or search_employers. No alternatives are mentioned, so usage context is only implied, not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.