extract_from_text

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 the extraction scope but fails to mention return format (e.g., array of matches), behavior on multiple/no matches, or whether extraction is case-insensitive or deduplicated.

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, front-loaded sentence that lists the core functionality and entity types without any wasted words, making it immediately scannable.

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 two-parameter extractor, the description covers the input semantics but omits the return shape, which is especially important since no output schema is provided. The missing output details leave some ambiguity for an agent consuming the result.

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 100%: both 'text' and 'type' have explicit descriptions and the enum lists all allowed values. The description adds minimal value beyond the schema, merely mapping 'phones' to 'phone numbers' and restating the entity types.

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 explicitly enumerates the target entity types (emails, URLs, numbers, hashtags, mentions, phone numbers), clearly distinguishing the tool from siblings like test_regex and extract_markdown_toc.

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 when to use the tool—whenever you need to extract these entity types from text—but it does not explicitly mention alternatives, prerequisites, or when-not-to-use conditions relative to sibling tools.

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