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

Beyond the readOnlyHint annotation, the description discloses non-obvious behavior: IDs are case-sensitive, symbols or wrong-case IDs are resolved via search, and the response names the actually fetched ID so a resolution is never silent. This is exactly the kind of behavioral context annotations do not provide.

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 sentences contain zero filler: the first front-loads the action and data returned, the second adds the resolution caveat essential for correct invocation. Every clause 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?

For a one-parameter, read-only tool with no output schema, the description is complete: it names the input forms, examples, case-sensitivity behavior, and the high-level return fields. An agent has enough to call it correctly and interpret the response.

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 goes beyond the schema by adding multiple examples and, crucially, the wrong-case resolution guarantee ('a resolution is never silent'), which clarifies what happens with non-exact inputs.

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 a specific verb ('Fetches'), a clear resource ('a coin by its CoinGecko coin ID or ticker symbol'), and the exact fields returned (price, market cap, ATH/ATL, description). This distinguishes it from sibling list/quotes tools like market_coins or market_quotes without needing to open their schemas.

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 intended use is clearly implied: use this tool when you need detailed single-coin data by ID or symbol. However, it never explicitly names sibling alternatives or states when-not-to-use it, so the agent must infer routing based on the tool name and description.

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