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Captia Industrial Knowledge

Search Captia knowledge base

search_knowledge

Full-text search across published editorial resources: guides, comparisons and technical articles on industrial data, OT/IT integration, industrial AI and energy. Returns canonical URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results, 1-20. Defaults to 5.
queryYesSearch terms.
localeNoResponse language. Defaults to Spanish, the site primary language.

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must carry the behavioral disclosure burden. It discloses that the operation is a search (implying read-only) and states it returns canonical URLs, which is useful. However, it does not mention any side effects, rate limits, or error behavior, leaving some behavioral aspects unspecified. The disclosure of return type adds value beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, with the core action ('Full-text search') and scope ('published editorial resources') in the first sentence. The second sentence adds a critical output detail (canonical URLs) without unnecessary filler. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with three parameters (one required) and no output schema, the description covers the essential aspects: what is searched, what content types and topics are included, and what the output contains. It does not explain result structure or pagination, but given the simplicity (returns canonical URLs) and presence of a limit parameter in the schema, the description is largely sufficient. Minor gaps remain around result details, but they are not critical.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%: all three parameters (query, limit, locale) are documented in the schema. The description does not add extra meaning beyond what the schema provides, such as how the limit parameter affects result ordering or how locale affects output. Since the schema fully covers parameters, a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: full-text search across published editorial resources, specifying content types (guides, comparisons, technical articles) and subject areas. This distinguishes it from sibling tools like get_glossary_term or list_protocols, which have narrower scopes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is implied by the description ('full-text search across published editorial resources') but there is no explicit guidance on when to prefer this over alternatives or when not to use it. The tool is not differentiated from siblings with direct comparisons, leaving the agent to infer its role as a general search across multiple resource types.

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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TDQS

A4.2/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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.

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