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

Annotations already establish readOnlyHint=true and openWorldHint=false, and the description adds real value on top: it discloses the tool is free with no key and describes what the result contains (gap in pp, signal, sample-size quality). It stops short of noting limitations or how the signal is derived.

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 usage cue, then the action, then the parameters, then the free/no-key note. Every sentence carries information; the only slight cost is the somewhat chatty opening quoted phrasings.

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 compensates by naming the return contents, and it covers cost/auth ('Free, no key') and the two input paths. An agent has enough to call it correctly, though it doesn't explain the signal's scale or the sample-size quality grades.

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, but the description adds the either/or relationship the schema does not state explicitly: use a known baseRateId (listed in the enum) OR supply your own baseRateValue. That mutual-exclusivity guidance is genuinely additive.

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 verb and resource ('compares a market price with the historical base rate for a class of events') and names the concrete outputs (gap in pp, a signal, sample-size quality). This distinguishes it clearly from siblings like bayes_update or calculate_ev, which handle different computations.

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 explicit trigger phrasings ('how does this price compare to history', 'is the market ignoring the base rate') that tell the agent when to reach for this tool. It does not name alternatives or state when NOT to use it, 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.