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

Annotations already cover the safety profile (readOnly, non-destructive, idempotent, closed-world), and the description adds meaningful semantic guarantees beyond that: standings are not assertions of qualification, amounts are per-program figures not totals, and no provider is ranked the winner. It also notes the result indicates how the review can be saved. This is above the annotation baseline, though it doesn't explain return format or idempotency implications.

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 a single very long sentence followed by several short clauses. It's dense and somewhat front-loaded, but the long compound sentence is hard to parse quickly and the final sentences about standings, amounts, and ranking are fragmented. It earns its place but could be structured more cleanly.

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 a simple single-parameter tool with full schema coverage and annotations covering behavior, the description adds substantial value: what the result contains (read of what matters, programs worth time, what can wait) and semantic caveats. No output schema exists, so the description appropriately describes the return shape at a high level. It is close to complete for this complexity.

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% and the schema already documents the answers object thoroughly with keying, length limits, and an example. The description reinforces this by listing the fact categories (stage, funding, tools, company type, earlier answers) and noting that missing facts stay unknown, adding useful semantic context about partial data. Baseline is 3, so this earns a 4 for the extra framing.

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 states what the tool returns (a Programscape review for a founder built from their company facts) and names the query it answers ('which startup credits, programs or perks fit a company'). It is clear and specific, but it doesn't explicitly distinguish it from siblings like compare_programs or search_programs, relying on the reader to infer the difference.

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?

Usage is implied rather than explicit: the tool is for questions about which credits/programs/perks fit a company. There's no explicit when-not-to-use guidance or direct routing to alternatives such as search_programs or compare_programs, which would be needed for a higher score.

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