literature.extract_claim

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

No annotations are provided, so the description carries the full burden. It discloses that the tool uses LLM extraction, returns span-grounded claims with provenance and confidence, and that optional parameters are soft signals. This provides sufficient behavioral context for an agent.

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 sentences with no wasted words. The first sentence efficiently states the core purpose and outputs, and the second adds key parameter details. Front-loaded and easy to parse.

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?

The description covers the main extraction purpose, optional parameter roles, and behavioral traits (soft signals). It is fairly complete given no output schema and five parameters, though details on document format or target_entities are missing.

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 0%, but the description adds meaning for disease_context and phenotype_keywords as disambiguation and ranking aids. Other parameters (documents, session_id, target_entities) lack explanation, so the description only partially compensates for the lack of 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?

The description clearly states the tool's purpose: high-throughput LLM extraction of claims with specific fields (source, target, sign, etc.) and provenance+confidence. It uses specific verbs and outputs, distinguishing it from siblings like literature.compile_constraints.

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 explains that optional parameters disease_context and phenotype_keywords disambiguate references and rank claims, and clarifies they are 'soft signals, never filters.' However, it does not explicitly compare to sibling tools or state when to use this tool versus others.

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