research_topic

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

With no annotations, the description carries the transparency burden and largely succeeds: it discloses the external YouTube search, the max_videos cap, transcript extraction, and ingestion into the knowledge base. It also states the return artifact. It doesn't mention failure modes or rate-limit implications, but the core behavioral contract is clear.

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 structured into a summary, workflow context, and an Args/Returns block, making it easy to scan. A few phrases are slightly redundant (the first two sentences both describe searching and mining), but no filler sentences exist.

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 tool with no annotations and no output schema, the description covers what it does, how it changes state, the meaning of every parameter, and what is returned. It lacks edge-case details such as what happens when no transcripts are found or the actual server cap, but those are not needed to invoke the tool correctly.

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 descriptions are absent (0% coverage), and the description compensates by defining all three parameters with meaningful detail: topic with an example, max_videos as a server-capped count, and languages as optional comma-separated preferences. This goes well 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 opening sentence specifies a concrete action ('research a topic') and a distinctive method ('mining transcripts of top YouTube videos'), separating it from generic search or retrieval tools. It also identifies the knowledge-base ingestion side effect, so an agent can recognize it as a learning/research capability.

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 explicitly frames this as the way the agent learns a subject and instructs using search_knowledge afterward, giving a clear follow-up workflow. It doesn't spell out when to prefer get_transcript or when not to use this tool, so it stops short of a full decision rule.

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