get_market_overview

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

No annotations provided, so description carries the full burden. It lists the returned fields but does not mention any side effects, rate limits, or data freshness. Adequate for a read-only tool but lacks behavioral depth.

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?

Two concise sentences with no wasted words. Front-loaded with the core action and immediately followed by usage context. Every word earns its place.

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?

Lacks output schema, so the description should explain return format more. It mentions returned fields but not ordering, pagination, or data source. Adequate for a simple tool but not fully complete for an autonomous agent.

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%, with the single parameter 'limit' described as 'Top N by mcap.' The description repeats this but adds no new details (e.g., default value, range), so it does not go beyond the schema.

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?

Description clearly states 'Top-N tokens by market cap' with specific attributes (volume, 24h change, category tags), which distinguishes it from sibling tools like 'get_price' or 'get_sentiment' that focus on narrower data.

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?

Explicitly tells when to use: 'for market-wide context (bull/bear, mover spotting).' Does not list exclusions or alternatives, but the context is clear enough for an agent to choose this tool over others.

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