extract_emails

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does (extract emails from text) but doesn't describe how it behaves: e.g., whether it returns all emails or just the first, handles malformed text, requires specific text formats, or has performance considerations. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational traits.

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 in Spanish: 'Extraer direcciones de email de un texto.' It is front-loaded with the core action and resource, with no wasted words or redundant information. This makes it easy for an agent to parse quickly and understand the tool's basic function.

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's simplicity (one parameter, no output schema, no annotations), the description is minimal but adequate for basic understanding. However, it lacks completeness for effective use: no output details (e.g., format of extracted emails), error handling, or behavioral context. Without annotations or an output schema, the description should provide more operational guidance to compensate, which it doesn't do.

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?

The description implies the 'text' parameter is the input from which emails are extracted, aligning with the schema's 100% coverage. However, it adds no additional semantic context beyond what the schema provides (e.g., text length limits, encoding requirements, or examples of valid inputs). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate with extra parameter insights.

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: 'Extraer direcciones de email de un texto' (Extract email addresses from a text). It specifies the verb 'extraer' (extract) and the resource 'direcciones de email' (email addresses) from the input 'texto' (text). However, it doesn't distinguish this tool from potential sibling tools like 'extract_urls' or 'extract_domain', which perform similar extraction operations on different data types.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'extract_urls' for URLs or 'extract_domain' for domains, nor does it specify use cases, prerequisites, or exclusions. The agent must infer usage based solely on the tool name and description without explicit context.

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