list_protocolsThe industrial protocols Captia Connect ingests from, with family and summary. Call it before compare_protocols to learn the valid slugs.
| 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, the description explains the read-style behavior: it lists ingested protocols with family and summary and exposes valid slugs. It is not as detailed as a full behavioral contract (e.g. ordering, response shape), but the resource and purpose are clear.
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?
Two short, front-loaded sentences with no filler. The first states the tool's result and the second gives the actionable relationship to compare_protocols.
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 optional-param list tool without an output schema, the description gives enough content expectations (family, summary, slugs) and the context for why the tool matters. It could only be more complete with a concrete output example or field list.
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% and the single locale parameter is fully documented there, so the description adds nothing about parameters. Baseline 3 applies because the schema already carries the meaning.
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?
States a specific verb ('list') and resource ('industrial protocols Captia Connect ingests from'), and adds what is returned (family, summary). It also names the sibling it feeds, distinguishing list_protocols from compare_protocols.
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?
Explicitly tells the agent to call this before compare_protocols to learn valid slugs, which is clear usage context. It does not discuss when not to use it or alternatives beyond compare_protocols.
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.