ai_text_chunker

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 does not explain how 'semantic' chunking works, what strategies actually do (e.g., are they truly semantic or just boundary-based?), performance characteristics, or any side effects. This leaves significant ambiguity for an agent.

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 sentence that is directly informative and front-loaded with the core action. Every word serves a purpose, with no repetition or fluff.

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?

Despite high schema coverage, the description does not explain the output format (e.g., array of chunk strings with metadata) or critical behavioral details like how semantic boundaries are determined. For a tool with 4 parameters and no output schema, the description should provide more context for an agent to use it effectively.

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 input schema has 100% description coverage for all 4 parameters (text, strategy, chunkSize, chunkOverlap), including defaults and enum values. The description adds no additional meaning beyond the schema, 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 clearly states the verb ('Split') and resource ('large text') and specifies the purpose ('for LLM context optimization or vector databases (RAG)'). It is distinct from sibling tools, which are data conversion, formatting, and utility tools, not related to text chunking.

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 some context (LLM, RAG) but lacks explicit guidance on when to use this tool versus alternatives or when not to use it. It does not mention the different chunking strategies (character, word, sentence, paragraph) or how to choose chunkSize/chunkOverlap. The usage is implied but not fully explicit.

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