search_companiesCompanies with a published page. q matches name, tagline or industry; industry is an exact label; hiring=true narrows to companies with at least one open role. Each item has open_role_count.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| size | No | ||
| limit | No | ||
| cursor | No | ||
| hiring | No | ||
| industry | No | ||
| location | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses meaningful behavior: only companies with a published page are returned, `industry` is an exact label, and `hiring=true` narrows results. However, it does not mention pagination, result ordering, whether filters combine, or how `cursor` and `limit` behave, leaving gaps for a search 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 three tight sentences, front-loads the core scope, and uses backticks for code identifiers. Every sentence adds either filter semantics or a useful return-field note; there is no filler or repetition of the schema.
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?
The definition covers the essential search behavior and mentions the notable output field `open_role_count`, and an output schema exists to document return values. Still, for a 7-parameter search tool with no annotations, it would benefit from at least a mention of pagination (`cursor`, `limit`) and how the filters combine.
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 0%, so the description must compensate. It does add meaning for `q`, `industry`, and `hiring`, explaining match semantics and exactness. However, it omits `size`, `location`, `limit`, and `cursor`, although those are partly self-explanatory from the schema's types, defaults, and enum values.
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 identifies the resource (companies with a published page) and the search behavior: `q` matches name, tagline, or industry, `industry` is an exact label, and `hiring=true` filters by open roles. It is specific enough to distinguish this from sibling search tools like search_people or search_jobs, and the 'Each item' phrasing signals a list-returning search.
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 no explicit guidance on when to use this tool versus alternatives like get_company or search_people. The filter semantics are described, but the description does not state when a user should prefer this tool, when they should use get_company for a single entity, or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.