get_glossary_termDefinition of an industrial term from the Captia glossary (42 entries: protocols, OT/IT concepts, industrial AI, energy). Accepts a slug or a visible label. If there is no confident match it returns close suggestions instead of a wrong definition.
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | Term to look up, slug or label. Example: "opc-ua", "OEE". | |
| 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 carries the behavioral burden, and it does disclose a key trait: on a low-confidence match it returns close suggestions rather than a wrong definition. It also clarifies accepted input forms, but stops short of describing the full response shape or edge cases like no suggestions at all.
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?
Three sentences with no filler. The resource and purpose come first, followed by input constraints and fallback behavior, each earning 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 low-complexity read-only lookup with full schema coverage, the description covers the resource, accepted inputs, and no-match behavior. The absence of an output schema is mitigated by the statement that it returns a definition or close suggestions, though a bit more detail on the suggestion format would make it fully complete.
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 coverage is 100%, with both parameters already documented: term has an example and type, locale has an enum and default. The description's 'slug or visible label' phrase largely restates the schema's parameter description, adding little new semantic value.
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 names the exact resource (Captia glossary), the action (returns a definition), and the accepted input forms (slug or visible label). The 42-entry scope and category list distinguish it from sibling tools like list_protocols or search_knowledge, so an agent can tell it apart without opening the schema.
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 intended use is implied: call this when you need a definition for a specific glossary term. However, it does not explicitly say when to prefer it over search_knowledge or list_protocols, nor does it state exclusions or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.