extract_facts

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'rule-based extraction' but does not detail limitations, required input quality, or the structure of extracted facts. This leaves significant behavioral ambiguity.

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?

The description is a single, efficient sentence that conveys the core purpose. It is front-loaded and avoids redundancy, but could benefit from additional structuring (e.g., bullet points or examples) for clarity.

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 tool with one parameter and no output schema, the description is minimally adequate. However, it lacks details on output format, error behavior, or typical use cases, which would enhance completeness.

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 one parameter ('text') described as 'Text to extract facts from'. The description adds context (observation text, rule-based) but does not compensate for the lack of deeper parameter semantics beyond the schema.

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 uses a specific verb ('extract') and clearly identifies the resource ('structured facts from observation text'). It distinguishes itself from sibling tools like 'detect_patterns' and 'semantic_search' by specifying 'rule-based extraction'.

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 implies usage for extracting facts from observation text, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., semantic extraction tools like 'semantic_search'). No 'when-not' or context for optimal use is given.

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