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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint=false), so the bar is lower. The description adds real value beyond them: it discloses the scoring is a deterministic rule-based heuristic, that only listed channels and the data/network flag affect the result, and that business_model is inert and echoed back. It stops short of describing output structure or any scoring breakdown.

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?

Three sentences with the core definition front-loaded, followed by the echo caveat and the disclaimer. The disclaimers are load-bearing (they prevent misuse as an investment signal), so the length is justified, though the phrasing is slightly dense.

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 no-output-schema, 4-param tool with full schema coverage, the description covers the essential behavior: what is scored, what is inert, and what the result means. It could say more about the return shape (just the number, or a breakdown), but an agent has enough 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?

Schema description coverage is 100%, so baseline is 3. The description adds a semantic layer the individual field docs don't: it clarifies which parameters actually move the score ('using only the acquisition channels you list and whether you have proprietary data or network effects') and that business_model is echoed rather than scored.

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?

States a specific verb and resource ('Rule-based 1 to 5 score of distribution defensibility') and pins down exactly which inputs drive it (acquisition channels plus the proprietary-data/network flag). This is clearly distinguishable from the calc_* financial siblings without opening any schema.

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 negative scoping is useful ('not a valuation, not an investment decision and not a prediction', 'discussion aid'), which tells the agent when not to over-rely on it. However, there is no positive routing: it never says when to reach for this versus calc_fundraising_readiness or get_mode_vc_thesis_and_criteria, so usage is implied rather than explicit.

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