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

Goes well beyond the readOnly/openWorld annotations by disclosing the ~7-day data lag, an explicit non-signal disclaimer, and an unusual billing model ($0.002 USDC per successful call via x402 on Base/Solana/Arc, failed calls uncharged). These are exactly the facts an agent needs before committing to a paid call.

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-loaded with what the data is, then caveats, then pricing; every clause carries distinct information. The opening sentence is dense and long, but nothing is filler.

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 an output schema present, the description needn't explain return shape, and it covers the remaining gaps an agent would care about: cohort semantics, data latency, domain restriction, and pay-per-call cost.

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% and already documents both `days` (14–365, default 90) and `cohort` (all/sth/lth); the description only restates the cohort expansions. No additional format, range, or interaction guidance is added, so the baseline 3 applies.

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 specific resource (Bitcoin SOPR daily series) with scope (all/sth/lth cohorts), the outputs returned (latest value, 7/30-day averages, days in profit vs loss), and the data source (bitcoin-data.com/BGeometrics). This is clearly distinguishable from sibling on-chain metrics such as agent_intel_onchain_mvrv_valuation without opening either schema.

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?

Gives clear operating context: Bitcoin only, free feed delayed ~7 days, and an explicit framing caveat ('Statistics only, not a trading signal'). It does not name alternative tools or say when to prefer MVRV/valuation siblings instead, so it stops short of a 5.

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