List RAG Chunks

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

Adds substantial context beyond the annotations: returns paginated chunks with fields (text, token count, metadata including quality grade, PII masking status, document type, language), explains the min_quality threshold behavior, and notes the indexing prerequisite. This complements the readOnlyHint and idempotentHint annotations without contradiction.

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 well-organized: a concise summary sentence followed by return details and a structured Args list. Every sentence adds value, and the length is appropriate given the 5 parameters.

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 covers the tool's purpose, prerequisites, parameter semantics, return content, and pagination guidance. With annotations and an output schema present, this is fully actionable for an agent to select and invoke the tool 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?

With 0% schema description coverage, the description fully compensates by documenting every parameter in the Args section, including defaults, allowed values, and an example for min_quality. This provides meaning far beyond the raw schema properties.

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 tool's function: 'Retrieve LangChain/LlamaIndex-ready text chunks from an indexed dataset.' This specific verb+resource+scope distinguishes it from siblings like dataset.search and dataset.export.

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?

Provides clear context: Pro+ plan restriction, the prerequisite that the dataset must be indexed via dataset.index(), and guidance to use FlexOrchRetriever/Reader for automatic pagination. However, it does not explicitly mention when not to use this tool or directly contrast with sibling tools.

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