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

Annotations cover the safety profile (readOnly, idempotent, non-destructive), and the description goes well beyond them: it discloses the hard-vs-ranking constraint split, the exact/relaxed return model, the geography relaxation rule, and, unusually, the entitlement behavior (5 rows in full plus locked.count/locked.by_type, contact values never returned). That is exactly the behavioral context an agent needs to set expectations.

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?

Front-loaded with the core matching model and constraint taxonomy, then usage examples, then access rules. Dense and largely earning its length, though the trailing promotional sentence with the pricing URL is commercial noise rather than tool guidance.

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?

No output schema exists, so the description carries the burden of explaining returns, and it does: match=exact vs match=relaxed rows, the named relaxed criterion, per-row venture fund lifecycle/vintage/state, and dfx:vc: identifiers. Combined with the entitlement/locked disclosure, an agent has everything needed to call and interpret this tool.

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?

With 75% schema coverage and 8 parameters, the description still adds real meaning: it enumerates which parameters are hard constraints (geography, sector, stage, amount_usd, active_only) versus ranking inputs, and clarifies geography semantics ('a city is its metro; Boston means the Boston area, the rest of Massachusetts is a relaxation') beyond the schema's terse field descriptions.

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 (find) and resource (VC investors) plus the exact scope: firms for a company raising a round, with hard constraints separated from ranking signals. It clearly distinguishes itself from the many search_vc_* siblings by describing the exact/relaxed matching model rather than a plain filtered search.

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?

Gives concrete invocation examples ('find investors for my Series A', 'Series A fintech investors in Boston', 'investors for a $7M AI infrastructure round') that make the intended use obvious. It does not, however, name or exclude any sibling tool, so the agent must infer when to prefer this over search_vc_firms or search_vc_raise_candidates.

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