chunk_text_semantic

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

With no annotations provided, the description carries the full burden. It discloses meaningful behavior: boundary-aware chunking, sliding context overlap, and the purpose of avoiding truncation. It does not describe output format or tokenization limits, but the core operational behavior is clear.

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?

A single focused sentence that front-loads the tool's purpose and method, followed by a concise price note. No filler or redundant restatement of the tool name.

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?

The description explains the high-level behavior and use case, but lacks details about input expectations, output shape, or practical boundaries. Since there is no output schema and generic parameters, an agent would still need to infer how to construct the payload correctly.

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 generic 'payload' and 'paymentSignature' parameters are documented at a basic level. The description adds context about what the processing does and mentions the USDC payment requirement, but it does not clarify the exact payload structure expected for chunking.

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 ('Performs'), a precise resource ('boundary-aware semantic token chunking'), and a clear objective ('eliminate chunk truncation in vector search'). This clearly distinguishes it from sibling tools like truncate_text_token_budget.

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 clearly implies when to use it: when token chunking is needed for vector search and truncation must be avoided. It does not explicitly name alternatives or exclusions, but the context is specific enough for an agent to differentiate.

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