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

Beyond the annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false), the description adds crucial behavioral details: each call tests exactly ONE hypothesis, costs ~$0.25/idea, and propagates from the seed idea into related concepts. It also explains the output: 'edge or no edge, stats, trade setup.' These are not present in the schema, providing genuine transparency.

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 ('BEST FOR', 'Returns', alternative tool). It is slightly verbose with marketing phrases like 'comprehensive market structure knowledge graphs ever built', but every sentence carries information. Front-loaded with the core purpose, and examples aid comprehension. A minor trim could improve conciseness, but it is not wasteful.

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 (6 parameters, output schema exists, annotations present), the description provides sufficient context for an AI agent to decide when to invoke it. It covers what the tool does, example usage, cost, output summary, and the distinction from a sibling tool. The output schema handles return values, so no further elaboration is needed. This is a complete description.

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 100%, so the baseline is 3. The description adds illustrative meaning to the 'topic' parameter via the 'momentum on grains' example, but does not enhance understanding of other parameters like 'context', 'markets', 'test_type', or 'search_mode'. It does not contradict or significantly add beyond the schema descriptions, so a 3 is appropriate.

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: 'Point VARRD's autonomous AI in a direction and let it discover edges for you.' It explains that it generates and tests hypotheses, and explicitly distinguishes this from the sibling tool 'varrd_ai' for controlled testing. The verb 'discover' and resource 'edges' make the primary function unambiguous.

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 'BEST FOR: Exploring a space broadly' and provides concrete examples of when to use it. It also gives a clear exclusion criterion: 'Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.' This is textbook usage guidance.

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