extract

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

No annotations provided, so description must disclose behavioral traits. It mentions LLM/heuristic modes but omits critical details like whether modifications occur, data persistence, rate limits, or error handling. Lacks output format information.

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?

Single, well-structured sentence with no redundancy. Every word adds value, and the key information (purpose, extracted items, methods) is front-loaded.

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?

Given the tool has multiple extraction dimensions and no output schema, the description should clarify the return structure or format. It lacks mention of output shape, limitations, or error cases, making it incomplete for an agent to fully anticipate results.

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 covers all parameters sufficiently (100% coverage). Description adds context about the extraction purpose but does not significantly enhance parameter meaning beyond schema descriptions.

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?

Description clearly states it performs 'Automatic semantic extraction from text' and lists specific extractable items: keyword, topic, domain, intent, sentiment, entities. It also specifies two modes (LLM or heuristic), making the tool's function unambiguous.

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?

Description provides no guidance on when to use this tool versus siblings like 'summary' or 'vector_search'. No explicit context for when not to use or alternatives, leaving the agent to infer usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.