chunk_content

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

The description discloses key behavioral traits: overlapping chunks, deterministic IDs from URL and position. No annotations are provided, so the description carries the full burden. It does not mention edge cases or side effects, but for a text transformation tool, the behavior is sufficiently transparent.

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 concise and front-loaded, with three sentences that each serve a purpose: stating the action, detailing the chunk characteristics, and suggesting use cases. No wasted words.

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, the description covers all necessary information: what it does, how it operates (overlap, chunk size, ID generation), and its intended use. No output schema exists, but the behavior is clear enough.

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%, so the baseline is 3. The description adds value beyond the schema by explaining how parameters are used: overlapping mechanics, deterministic ID derivation from URL, and the general split strategy, which enriches the meaning of the parameters.

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 action ('Split clean text into overlapping chunks'), specifies chunk size (~1000 characters) and overlap (200 characters), and distinguishes from sibling tools (cleaning, crawling, scraping) by focusing on the chunking process.

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 mentions use cases ('preparing text for vector embedding or search indexing'), providing clear context. However, it does not explicitly state when not to use the tool or compare to alternatives, though the sibling tool names imply distinct purposes.

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