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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint, non-destructive). The description adds value beyond this by disclosing the output behavior: 'Returns summarised, cited insights from a curated knowledge base of leading practitioners, books and research.' This sets accurate expectations for answer format and sourcing, which annotations alone would not convey. No contradiction with annotations.

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?

Two sentences with zero waste. The first sentence front-loads the verb and topic scope; the second adds return format and knowledge source. Every phrase earns its place, and the most decision-relevant information (what to ask about) comes first.

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 moderately simple tool (3 params, 1 required, no output schema), the combination of description, schema, and annotations is nearly complete: description covers purpose/topics/output format/sources, schema fully documents params and email behavior, and annotations cover safety. The main gap is the absence of an output schema, so return-structure expectations rest on the brief 'summarised, cited insights' phrase, and sibling routing is unaddressed.

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% — all three parameters (email, query, top_k) have rich descriptions, including the email quota distinction (30 vs 5 queries/day) and the 'ask the user once, then pass it on every call' guidance. The description adds no parameter-specific meaning beyond this; the schema does the heavy lifting, so the baseline 3 applies.

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 a clear verb+resource ('Ask the Ergora Business finance') and enumerates its scope: SaaS metrics, cash flow, valuation, fundraising, CFO. This distinguishes it from most siblings (marketing, sales, HR, legal, etc.). However, it does not explicitly differentiate itself from ergora_ask (the generic ask tool) or ergora_business_strategy, leaving some ambiguity among closely related specialists.

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 topic list (SaaS metrics, cash flow, valuation, fundraising, CFO) implies when to use this tool: finance-domain questions. But there is no explicit when-not-to-use guidance, no named alternatives, and no routing hints among the 18 siblings. An agent must infer that non-finance questions belong elsewhere.

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