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

Annotations already establish read-only, idempotent, non-destructive behavior心的的，但描述 adds valuable context: provenance-backed results, separation of modes, and a guarantee that incompatible measures are never summed ('never fabricates a total'). It also clarifies completeness reporting and percent change, which the agent could not infer from annotations alone.

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 with zero waste: the first states the core deliverable, the second details the output composition, and the third gives runtime examples and an exclusion. It is front-loaded with the main purpose and every sentence 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 no-output-schema tool, the description covers the key semantic ground: what it returns (evidence pack with totals, completeness, percent change), how data is kept separate, provenance, and what it deliberately does not do. It gives the agent enough to decide on invocation, interpret results, and set expectations about data integrity.

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% because all three parameters (port, response_format, trailing_months) have descriptions. The description adds no new parameter-level detail beyond schema descriptions—for example, 'trailing_months' is mentioned as a default but not elaborated. The baseline 3 is appropriate since the schema already does the heavy lifting.

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 uses a specific verb ('Combine'), resource ('every available official BTS crossing measure'), and scope ('one land-border port'), making it clear this is an aggregation pack. It distinguishes itself from BTS siblings by emphasizing a single port with current-versus-prior comparison dashboards and explicitly excludes congestion/wait-time measures.

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 provides concrete use cases like 'build a border activity pack for Laredo' and 'what changed at Otay Mesa?', plus a clear exclusion ('not a congestion or wait-time measure'). However, it does not explicitly name sibling tools like bts_transport_compare_ports or bts_transport_border_trend and explain when to choose them instead, leaving some inference required.

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