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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds genuinely new context: that these are per-market defaults and that they are implicitly applied to any analysis invoked without an explicit `models` argument, which is behavioral information no annotation provides.

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?

Three sentences, each contributing information: contents, fallback semantics, and trigger condition. The opening sentence is long and packs several fields together, but nothing is redundant or padded.

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 describing the return shape, and it does so by listing markets, models, timeframe, intervals and committee seats. Minor gaps remain: it does not say what happens if the user has no settings configured, or whether both markets are always returned.

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?

The tool takes zero parameters, so there is no parameter meaning to convey and the baseline is 4. The description correctly implies no inputs are required to read the full default setup.

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?

Names a specific verb (read/get) and resource (the user's default analysis setup), then enumerates exactly what the payload contains: models per market, timeframe, run intervals, and deep committee seats. This is clearly distinguished from the mutating sibling update_settings by the word 'default' and the read framing.

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?

'Read it when the user asks which models or timeframe their analyses use' gives an explicit triggering condition, and 'Every analysis started without `models` runs on it' explains the downstream relevance. It stops short of naming update_settings as the alternative when the user wants to change these values rather than inspect them.

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