get_company_profileIdentity of Captia Technology: legal entity, tax id, offices, contact, the five business units and declared areas of knowledge. Use it to answer who Captia is or what each unit does.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | Response language. Defaults to Spanish, the site primary language. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It is transparent about the kind of data returned: legal details, contact info, business units, and declared knowledge areas. It does not describe the response format or potential locale behavior, but for a profile-retrieval tool the content disclosure is strong.
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 two sentences with no filler. It front-loads the core identity content and then states the practical use case. Every part earns its place, and it is easy to scan.
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 tool with one optional parameter and no output schema, the description gives enough context for an agent to select and invoke it correctly. It explains what data will be returned and when to use it. It could mention the response shape or the default locale, but that information is either implied or available in the schema.
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 fully documents the only parameter (locale) with an enum and description, achieving 100% schema description coverage. The tool description does not add extra meaning about the locale parameter, so the baseline 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 purpose: providing the identity of Captia Technology, including legal entity, tax id, offices, contact, business units, and knowledge areas. It distinguishes itself from sibling tools like calculate_oee or search_knowledge by focusing specifically on company identity. The phrase 'Use it to answer who Captia is or what each unit does' reinforces the exact resource and intent.
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 when to use the tool: to answer questions about who Captia is or what its business units do. It provides clear usage context, though it does not explicitly mention when not to use it or name alternative tools for related but different queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct knowledge task: calculate, compare, company profile, glossary, list protocols, and search. The only minor overlap is between list_protocols/compare_protocols and get_glossary_term/search_knowledge, but the descriptions make their boundaries clear.
All tool names follow a consistent verb_noun snake_case pattern, with verbs like calculate, compare, get, list, and search accurately reflecting the action. There are no mixed conventions or vague verbs.
Six tools is a well-scoped set for an industrial knowledge server. Each tool covers a distinct function and none feel redundant or missing from the core set.
The core knowledge surface—company identity, glossary definitions, protocol listing/comparison, OEE calculation, and full-text search—is covered. Minor gaps exist, such as no dedicated single-protocol detail endpoint or article content retrieval, but agents can work around them.