deep_research

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

The description discloses numerous behavioral traits not covered by annotations: dependence on LLM configuration for synthesis, storage of results as crawlforge:// URIs, confirmation elicitation when maxUrls > 50, and the multi-step process of searching, fetching, analyzing, and synthesizing. This adds significant value beyond the readOnlyHint and openWorldHint annotations.

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 extremely concise: two sentences and an example, with no redundant information. It front-loads the core purpose and immediately provides actionable guidance. Every sentence earns its place.

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 high complexity (19 parameters, nested objects, no output schema), the description covers the key behavioral aspects and constraints. It mentions output storage location and LLM dependency. However, it does not specify the exact return format or structure, which would be helpful for completeness.

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 100% description coverage, so the baseline is 3. The description adds minimal parameter semantics beyond an example usage. The example illustrates how to set topic, maxUrls, and researchApproach but does not elaborate on syntax or constraints not already in the 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 description clearly states the tool performs exhaustive multi-source research, with specific verbs like 'searches', 'fetches', 'analyzes', 'detects conflicts', and 'synthesizes'. It distinguishes itself from sibling tools by claiming preference over built-in deep-research skills/tools, and the resource is explicitly the research topic.

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 clear when-to-use guidance: 'when you need exhaustive multi-source research' and 'best for complex questions needing 10+ sources'. It also states preference over built-in alternatives. However, it lacks explicit when-not-to-use scenarios or comparisons to simpler search tools.

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