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

Goes well beyond the readOnly/openWorld annotations by disclosing the cost model (0.015 USDC per call via x402), the hard cap of 300 chunks, and the exact embedding model and dimensionality. These are real operational facts an agent needs. It stops short of describing failure modes or latency.

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?

Front-loads the outcome ('Ready-to-search chunks') and packs model, limits, and pricing into tight sentences with no filler. Slightly dense, but every clause carries 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?

With no output schema, the description carries the burden of explaining returns and does so (chunks with offsets + embeddings). Combined with the disclosed chunk cap and cost, an agent has enough to invoke and budget. Edge-case behavior (invalid URL, oversized input) is not covered.

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 60%: embed, overlap, and chunk_size carry descriptions, while url and text do not. The description compensates partially by noting URL accepts pages, PDFs, and Office files and that chunks overlap, but gives no acceptable ranges or defaults beyond what the schema already states.

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?

States a concrete verb+resource (turns a URL or text into chunks) and details the full pipeline output: overlapping chunks, character offsets, and bge-m3 1024-dim embeddings. It is highly specific about what is produced, but does not explicitly distinguish itself from embedding siblings like embed/embeddings/vectors.

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?

Implies usage via 'ready for any vector store' and 'in one call,' suggesting it is the combined extract+chunk+embed step. However, it names no alternatives and gives no explicit when-to-use vs when-to-use-something-else guidance despite many closely related siblings (embed, embeddings, scrape, crawl).

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