get_knowledge_base_stats

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'detailed statistical information' and implies read-only behavior by focusing on analysis. However, it lacks details on potential side effects (e.g., if it triggers background processing), performance characteristics (e.g., latency for large knowledge bases), or error conditions. The description adds some context but doesn't fully cover behavioral traits beyond basic functionality.

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 well-structured with a purpose statement, usage scenarios in bullet points, and an output note. Each sentence earns its place by clarifying different aspects (what it does, when to use it, what it returns). It could be slightly more concise by integrating the output note into the purpose, but overall it's efficient and front-loaded with key information.

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 the tool's low complexity (0 parameters) and the presence of an output schema, the description is reasonably complete. It covers purpose, usage, and output at a high level, which is sufficient for a stats-retrieval tool. However, without annotations, it could benefit from more behavioral details (e.g., idempotency, rate limits), but the output schema likely handles return values, reducing the need for extensive description.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description correctly omits parameter details, focusing instead on usage and output. This aligns with the baseline of 4 for zero parameters, as it avoids redundancy and adds value through context rather than repeating schema information.

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 purpose: '获取有关知识库的综合统计信息' (Get comprehensive statistics about the knowledge base). It specifies the types of statistics (document types, processing methods, structural information) and distinguishes itself from siblings like 'get_embedding_cache_stats' or 'get_vector_database_stats' by focusing on knowledge base content rather than caching or database metrics. However, it doesn't explicitly contrast with all siblings (e.g., 'ask_rag' is for querying, not statistics).

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 explicit usage scenarios in a bulleted list: checking document count, understanding file type distribution, viewing processing methods, and analyzing structural complexity. This clearly indicates when to use this tool—for statistical analysis of knowledge base content—and implicitly distinguishes it from alternatives like querying tools ('ask_rag') or maintenance tools ('reindex_vector_database'). No exclusions are stated, but the context is well-defined.

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