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, and it does so well. It reveals that the tool decomposes the query into sub-queries, searches and reads multiple sources in parallel, costs 50 credits, and returns specific fields including credits_used and usage.

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 information-dense but well organized, front-loading the core behavior and usage guidance before costs, returns, and parameter docs. The Args and Returns sections make it easy for an agent to scan.

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 having no output schema or annotations, the description covers purpose, usage criteria, cost, return shape, and all parameter semantics. An agent has sufficient context to decide when to invoke research and how to call it 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?

The input schema has 0% description coverage, but the tool description compensates fully by explaining every parameter. It defines query, describes topic choices ('general' vs 'news'), gives concrete freshness formats, and specifies max_sources with range and default.

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 opens with a specific verb and resource: 'Perform comprehensive research on a topic' and details the decomposition, parallel reading, and synthesized report with citations. This clearly distinguishes it from siblings like search, extract, and fetch.

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?

It explicitly says when to use the tool: 'Best for open-ended or comparative questions that need coverage from many angles.' It also points to the alternative for simple cases: 'For simple factual lookups, use search instead,' giving clear when/ when-not guidance.

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