chunk_text

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds behavioral detail beyond annotations: the algorithm ('uniform sliding-window segments with configurable character overlap') and the return format ('Returns an array of formatted text chunks'). This provides useful context without contradicting the annotations.

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 compact—two sentences total—with no redundant phrasing. The primary action and output are front-loaded, followed by usage guidance and exclusions. Every sentence contributes new information.

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 presence of an output schema (as indicated by context signals) and full parameter documentation in the input schema, the description covers purpose, usage, parameters, and return type. It leaves no critical information missing for an agent to correctly invoke the tool.

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%, so all parameters (text, chunk_size, chunk_overlap) are already well-documented with descriptions, defaults, and constraints. The description adds no additional parameter meaning beyond what the schema provides, so it meets the baseline of 3.

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 states a specific verb ('Partitions'), a clear resource ('raw text documents'), and the output ('array of formatted text chunks'). It is clearly distinct from sibling tools like audit_dns, eval_expression, extract_web, repair_json, and validate_schema, which have no overlap with text segmentation. The purpose is unambiguous.

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 explicitly provides when-to-use ('preparing unstructured documents for vector database embeddings and RAG retrieval pipelines') and when-not-to-use ('Do not use for syntactic token counting or semantic sentence segmentation'). This gives an agent clear decision criteria, though it does not name an alternative tool, the exclusions are sufficient.

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