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

The description goes beyond the all-false annotations by disclosing that the operation costs 1 credit, that it strictly uses existing research rather than new market data, and that parent research must be complete. It also notes that it returns the full analysis, which is valuable given there is no output schema.

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 dense but every sentence earns its place: purpose, source constraint, parameter clarification, prerequisite, cost, and return value. The most important information is front-loaded, and there is no repetition of schema fields.

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?

For a tool with no output schema and moderate complexity, the description covers the essential operational context: what it produces, what it requires, how it is sourced, what it costs, and how the target parameter behaves. Nothing critical is missing for an agent to decide whether to invoke 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?

Schema description coverage is 100%, so the schema already documents both parameters well. The description reinforces the 'target' semantics and the omit-default behavior, but adds little beyond the schema's own detailed descriptions. A baseline 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 starts with a specific verb and resource: 'Generate an in-depth framework analysis' for a completed research. It enumerates concrete components (jobs-to-be-done, positioning, MVP scope, Kano, pricing, 90-day plan, risks) and clearly distinguishes itself from research creation by requiring the parent research to be complete.

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 clearly states when to use the tool: only after research is complete, and only seeded from that research without new market data. It does not explicitly name alternatives like retry_deep_dive or create_research, but the precondition 'Requires the parent research to be complete' provides clear contextual guidance.

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