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

With no annotations, the description carries the full burden, and it discloses the price ($0.10 paid in USDC via x402), a key behavioral and financial constraint, plus the exact output components including the fact that a best-fit pool may be absent ('if any'). It omits failure modes and empty-result behavior but covers cost, payment, and the non-mutating nature via the word 'read'.

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 dense sentence that front-loads the tool's identity and output sections before the cost. The colon-separated list and payment clause are all essential, with no filler words.

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 six optional parameters and no output schema, the description adequately sketches return structure and cost, but it leaves filter semantics (AND vs OR, optionality of chain) and no-match behavior unspecified. An agent would still need to infer how the optional filters combine to form a valid request.

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 baseline is 3. The description only says 'matching a filter' and adds no detail beyond the schema's per-parameter descriptions, so it doesn't improve semantic understanding of chain, limit, symbol, project, stablecoin, or min_tvl_usd.

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 it is an LLM-written read of DeFiLlama yield pools and enumerates the deliverable sections (verdict, key points, risks, best-fit pool, caveats, pool rows), which makes the tool's function and output clear. It doesn't name a sibling, but 'LLM-written' implicitly separates it from raw data tools like defi_yields.

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 implies this tool is for narrative analysis rather than raw scraping or protocol metadata, but it never explicitly states when to prefer it over siblings such as defi_yields or scrape_summary. No exclusions or alternative conditions are provided.

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