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

With no annotations, the description carries the full behavioral burden. It discloses the structure of each series (recent points, normal, ratio, normal_ready), explains when normal_ready is false (baseline too short), and explains the ratio_note condition (normal too small to divide by). It even adds operational context (AIS collectors hourly, chokepoints passing 2-3 ships a day). This is thorough disclosure of edge cases and logic, beyond what annotations would 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?

The description is long but densely packed with essential information. It front-loads the core purpose ('measured series for one country') and then expands with a list of series types and data structure. Every clause contributes value (e.g., the ratio_note condition is explained). No fluff exists, though it could be trimmed slightly, the detail is justified by the tool's complexity.

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 no output schema, the description adequately explains what the tool returns: the series types and the per-series fields. It also covers edge cases (normal_ready, ratio_note) and operational context. An agent can confidently invoke this tool and interpret results without ambiguity. Missing details like authentication are irrelevant for a read-only data retrieval tool.

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 baseline is 3. The description adds meaning beyond the schema: it clarifies that 'id' comes from a previous answer (implying a chaining workflow) and reinforces country as ISO alpha-2. This is useful context that helps the agent understand how to populate parameters, exceeding the baseline.

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 the resource ('the measured series for one country') and enumerates the specific series types (aircraft overhead, military aircraft, ships, IPv4 space, etc.), making the tool's function unmistakable. However, it does not explicitly differentiate from sibling tools like geo_activity or geo_snapshot; the differentiation is implicit through the term 'series' and the list of contents. This is strong but lacks a direct sibling distinction.

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 implies usage: an agent needing time-series data for a country with baselines and ratios would select this tool. It does not provide explicit 'when to use' vs 'when not to use' guidance, nor name alternatives. The context of siblings exists but is not addressed. Thus, usage context is implied rather than spelled out.

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