Get a data overview showing snapshot count, total book records, book_id coverage, and discovered data directories to assess dataset completeness before analysis.
数据概览：快照数量、书目总数、book_id 覆盖率、被探测到的数据目录。
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose the scope of the output (snapshot count, book_id coverage, detected directories), which is useful. However, it does not mention the return format, whether the operation is purely read-only, or any other behavioral traits such as scanning/probing implications.
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 a single compact sentence that front-loads the category ('数据概览') and then lists the specific data points it covers. There is no redundant or filler content.
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 zero-parameter, simple stats tool with no output schema, the description names the key pieces of returned information. It is adequate for an agent to understand what invoking this tool will provide, though it could be slightly stronger with an explicit statement of its read-only nature or output structure.
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 has zero parameters and the schema is empty, so parameter explanation is not needed. The description adds context about what the tool reports, which is sufficient given there is nothing for the caller to configure.
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 identifies the tool as a data-overview/stats tool by naming specific metrics: snapshot count, total bibliography count, book_id coverage, and detected data directories. This aligns with the tool name 'fanqie_stats' and distinguishes it from ranking/find/trend siblings in substance, though it does not explicitly name or contrast them.
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?
There is no guidance about when to choose this tool over the sibling tools such as fanqie_ranks, fanqie_find, or fanqie_trend. The description only implies an overview/statistics use case, with no explicit when-to-use, when-not-to-use, or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.