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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful context (data source, cost per call) but does not disclose output format, time period, or limitations. This is adequate but not rich behavioral disclosure.

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?

Four sentences are front-loaded with the primary use case, then data, source, audience, and cost. The audience sentence adds some context but is not strictly necessary. No obvious bloat.

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?

The tool has no output schema, so the description should describe return content; it does identify the metrics returned. However, it omits time period, data vintage, and exact response shape, leaving an agent to infer some details.

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 0% schema description coverage, the description carries the burden. It maps the two dimensions (industry and deal size) to likely parameters, but does not explain the meaning of enum values like deal_size_tier thresholds. It adds high-level semantics only.

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 names a specific verb/resource: M&A transaction multiples with concrete metrics (EV/EBITDA, EV/Revenue, control premiums) and dimensions (industry, deal size). It clearly distinguishes from sibling tools like get_public_market_multiples by focusing on deal transactions rather than trading multiples.

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?

It explicitly frames when to use the tool: valuing an acquisition target, benchmarking deal pricing, or preparing a fairness opinion. It does not list alternatives or exclusion criteria, but the use cases are specific enough to guide selection.

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