投递数据看板

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

No annotations are provided, so the description carries the full behavioral burden. It says what metrics are computed but never states that the operation is read-only, what scope/timeframe the stats cover, how fresh the data is, or what the response shape looks like — significant gaps for a tool with zero annotation coverage.

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?

A single tight sentence that front-loads the resource and then lists the computed metrics; every clause earns its place with no redundancy.

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?

With no parameters and no output schema, the description partly compensates by enumerating the returned metrics (status counts, conversion rate, average match score). It is close to sufficient for a zero-arg read tool, though the lack of any scope or time-window note leaves a small gap.

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?

The tool takes zero parameters, so there is no parameter syntax to document; per the rubric this earns the baseline 4. The description correctly implies no filtering inputs are required.

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?

States a specific verb+resource (统计投递漏斗) and enumerates the metrics returned (各状态数量、转化率、平均匹配分), so the agent knows this is an aggregate-stats tool, not a record listing. It is distinguishable from application_list by being a funnel summary, though it never names the sibling explicitly.

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 purpose implies usage (call this to see funnel/conversion metrics), but there is no explicit when-to-use statement, no exclusions, and no reference to alternatives like application_list for per-record data. Usage is only inferable.

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