auto_research_topic

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

With no annotations provided, the description fully discloses the tool's behavior: it slugifies the topic, searches arXiv and Semantic Scholar, ingests into Zotero and Obsidian, bundles/upload/generates/downloads a NotebookLM brief. It also explains the conditional crystal generation and lists the return fields. No contradictions with annotations (none present).

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 summary line, detailed process, usage hint, and return format. It is not overly verbose, though some sentences are complex. It front-loads the essential purpose.

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?

Despite many siblings and 14 parameters, the description covers the core process, preconditions for crystals, and return fields. It does not explain all parameters or edge cases, but given the output schema exists, it is reasonably complete.

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 description coverage is 0%, so the description must add meaning. It explains some parameters like topic, cluster_slug, do_crystals, and do_nlm implicitly, but many parameters (e.g., field, max_papers, dry_run, thresholds) are not described. This leaves significant ambiguity for the agent.

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 as a one-shot research pipeline combining search, ingestion, and NotebookLM brief generation. It explicitly mentions the situations to use (e.g., user says 'research X for me'). This distinguishes it from siblings like search_papers or add_paper which are individual steps.

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 a clear usage context ('Use when: user says "research X for me"'), but does not explicitly mention when not to use this tool or compare it to alternatives from the sibling list. It is still helpful for selecting the tool.

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