auto_research_topic

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

No annotations provided, so description carries full burden. It discloses key behaviors: slugification/reuse of cluster_slug, multi-source search, ingestion pipeline, conditional crystal generation with LLM CLI requirement, and return value structure. However, it doesn't mention potential side effects like creating new clusters or modifying existing ones beyond reuse.

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?

Description is compact yet comprehensive. Front-loaded with the core pipeline, each sentence adds distinct value: purpose, slug logic, actions, crystal condition, usage cue, and return format. No wasted words.

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 14 parameters and no annotations, the description covers the main workflow and return values but omits explanations for most parameters. It provides a good high-level overview but lacks depth for configuration options, making it only partially 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 coverage is 0%, so description must compensate. Only topic, cluster_slug, and do_crystals are briefly explained. The other 11 parameters (max_papers, field, dry_run, etc.) are not described, leaving the agent without guidance on their purpose or effects.

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 defines the tool as a 'one-shot research pipeline' specifying actions: search, ingest, NotebookLM brief, and optional crystals. It differentiates from sibling tools by combining multiple steps, with explicit mention of searching arXiv + Semantic Scholar, ingesting into Zotero + Obsidian, and generating a NotebookLM brief.

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?

Explicitly states 'Use when: user says "research X for me" or "find papers on X".' This gives clear context for usage, though it does not provide exclusions or alternatives for when a simpler tool would suffice.

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