search_knowledge

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

With no annotations, the description carries the behavioral burden, and it does so well: it clarifies this is a retrieval operation, describes the returned artifact (passages with deep links and timestamps), and frames the intended follow-up behavior (answer using them as evidence). It does not discuss auth, rate limits, or failure modes, but for a read-only search tool this is suitably 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 compact, front-loaded with the core operation, then adds output details, usage instruction, and an Args block. Every section adds distinct value: the Args block is especially helpful given the empty schema descriptions.

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 no output schema, the description still explains what will be returned: most relevant transcript passages, each with a deep link and exact timestamp. It also gives enough context to use the tool responsibly, though it could more explicitly differentiate from get_transcript for agents deciding between retrieval tools.

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?

Schema coverage is 0%, so the description is the only source of parameter meaning, and it fully compensates. It explains query as natural-language/keywords, k as number of passages, and topic as a filter tied to a prior research_topic invocation—each more informative than the bare schema titles.

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?

Names a specific operation ('Semantic/keyword search') and resource ('everything the agent has already learned', 'local knowledge base', 'transcript passages'), with a clear outcome: retrieve relevant passages with deep links to source videos. This distinguishes it from siblings like get_transcript (raw transcript retrieval) and research_topic (topic creation).

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 gives clear context: use this when answering a user's question from previously learned material. It instructs the agent to use returned passages as evidence. It does not explicitly name alternatives or when-not-to-use cases, so it falls just short of full guidance.

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