air_quality_watch_point

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds limited extra transparency by specifying the data source URL and the return type (pandas.DataFrame). It does not describe edge cases, potential failures, or detailed output structure, but given the annotations cover safety, the added context is adequate.

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 poorly structured: it begins with a redundant title line, includes a broken sentence ('指定之间段之间的空气质量数据'), and mixes Chinese and English unevenly. The docstring-style parameter sections are organized, but the front matter contains unnecessary repetition and awkward phrasing, making it less concise and clear than it could be.

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?

The description covers the core aspects: purpose, parameters, return type, and source URL. However, without an output schema, it gives only a high-level description of the return ('观测点空气质量') and does not specify columns or metrics. For a simple retrieval tool with three string parameters, this is acceptable but not comprehensive.

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?

With 0% schema description coverage, the description fully compensates by providing parameter types, format examples for dates, and a lookup method for city values. This is highly valuable for an agent to construct valid calls. The only minor gap is not explicitly stating the date format beyond examples.

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 tool's function: retrieving air quality data for each monitoring point in a specific city within a date range. The phrase '细化到具体城市的每个监测点' highlights the per-station granularity, which distinguishes it from sibling tools like air_quality_hist or air_quality_rank. However, it lacks an explicit verb like 'get' or 'fetch', and the phrasing is somewhat awkward.

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 usage context is implied through the description of per-station data, and the parameter doc for 'city' explicitly instructs users to call ak.air_city_table() to obtain valid city values. No explicit alternatives or exclusions are provided, making it unclear when to choose this tool over sibling air quality tools.

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