search_pdf_knowledge

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

The description discloses the vector similarity approach and that it searches indexed PDF chunks, implying read-only operation. However, it lacks details about permissions, rate limits, or response structure. Annotations are absent, so the description carries the full burden but is only partially transparent.

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 concise, starting with the purpose in the first sentence, followed by technical detail and parameter documentation. No unnecessary words or repetition.

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 an output schema exists, the description need not explain return values. It covers parameters well and mentions indexing. However, it does not mention prerequisites like database availability or how to list databases, and lacks error handling context.

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?

All three parameters are explained beyond the schema: 'query' as natural language, 'databases' with examples of slugs and default behavior, and 'top_k' with default 8. Schema description coverage is 0%, so the description fully compensates.

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 performs semantic search across knowledge database layers, specifies it uses 768-dim nomic-embed vector similarity, and provides examples of layers like 'PH background' and 'HAT specialist'. This distinguishes it from other search tools by focusing on PDF chunks and vector similarity.

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?

No explicit guidance on when to use this tool versus alternatives like semantic_search or search_library. The description only explains what it does, not when it is appropriate or when to choose another sibling.

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