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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, indicating safe behavior. The description adds that data is 'live from a community mirror' and 'compact', providing extra context about freshness and size. No contradiction with annotations.

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?

The description is three sentences, uses natural language with example queries, and avoids unnecessary details. Every sentence adds value: purpose, output, and source. Front-loaded with the key action.

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?

Given zero parameters and no output schema, the description fully explains what the tool returns and its source. The agent can accurately predict results without additional documentation.

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?

There are zero parameters, and the input schema is fully covered (100% coverage). The description compensates by explaining the output fields (active train count, train numbers, worst delay) and overall behavior, which is sufficient for agent understanding.

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 clearly states it provides a summary of all currently active Amtrak trains grouped by route, with example queries. It differentiates from sibling tools like amtrak_station_board (station-specific) and amtrak_train_status (individual train) by focusing on a national system overview.

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 description includes example queries ('how many Amtrak trains are running right now', 'which Amtrak routes have delays') which imply usage context. It does not explicitly state when not to use it or compare to siblings, but the purpose is clear enough for an agent to infer appropriate scenarios.

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