ask_rag_filtered

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 describes the tool's filtering behavior well with specific examples, but doesn't mention other behavioral aspects like response format, error handling, rate limits, or authentication requirements. The description adds value by explaining filtering logic but lacks comprehensive behavioral disclosure.

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 and appropriately sized. It starts with a clear purpose statement, provides usage scenarios with bullet points, then lists parameters with explanations. Every sentence earns its place, and there's no redundant information. The bilingual nature (Chinese with English examples) is efficient for the intended context.

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 complexity (5 parameters, filtering logic) and the presence of an output schema (which handles return values), the description is mostly complete. It explains the purpose, usage, and parameters thoroughly. The main gap is lack of behavioral context beyond filtering (e.g., performance characteristics, limitations), but the output schema reduces the need to describe return values.

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 detailed parameter explanations. Each parameter is clearly explained with examples: 'query: 要向知识库提出的问题或查询' (the question or query to ask the knowledge base), 'file_type: 按文件类型过滤（例如 ".pdf", ".docx", ".txt"）' (filter by file type, e.g., ".pdf", ".docx", ".txt"), etc. The description adds significant meaning beyond the bare schema.

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: '向 RAG 知识库提问，并使用特定过滤器聚焦搜索' (Ask the RAG knowledge base and use specific filters to focus the search). It specifies the verb ('提问' - ask/query) and resource ('RAG 知识库' - RAG knowledge base), and distinguishes it from the sibling 'ask_rag' by explicitly mentioning filtering capabilities.

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 with concrete examples: '仅搜索 PDF 文档：file_type=".pdf"', '查找包含表格的文档：min_tables=1', etc. It clearly indicates when to use this tool (for filtered searches) versus the sibling 'ask_rag' (presumably for unfiltered queries), making the distinction clear.

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