research_start

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the asynchronous nature (requires polling research_status), time estimates for modes (fast ~30s, deep ~5min), result volume estimates (~10 vs ~40 sources), and constraints (deep mode is web only). It doesn't mention error conditions or rate limits, but provides substantial operational context.

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 clear sections (purpose, usage, workflow, parameters) and front-loaded key information. Every sentence adds value, though the 'Args:' section could be slightly more concise by integrating examples directly with parameter explanations rather than as separate examples.

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 the tool's complexity (asynchronous operation with multiple parameters and modes), no annotations, and the presence of an output schema (which handles return values), the description provides complete contextual information. It covers purpose, workflow, parameters, behavioral expectations, and usage guidelines, leaving no significant gaps for agent understanding.

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?

With 0% schema description coverage, the description fully compensates by providing clear semantic explanations for all 5 parameters. It explains what each parameter controls (query for search terms, source for location, mode for speed/depth, notebook_id for existing/new notebooks, title for naming), including practical examples and constraints like 'deep mode is web only'.

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 with specific verbs ('search web or Google Drive to FIND NEW sources') and distinguishes it from siblings by focusing on initiating research workflows. It explicitly mentions the workflow sequence (research_start → poll research_status → research_import), which differentiates it from other tools like notebook_create or source_list_drive.

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 explicit usage guidance with 'Use this for:' examples ('deep research on X', 'find sources about Y', etc.) and clearly outlines the workflow context. It distinguishes when to use this tool versus alternatives by specifying the research workflow sequence, though it doesn't explicitly name alternative tools for similar functions.

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