ExitValue.ai M&A Multiples

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

Annotations already establish that this is a read-only, idempotent, non-destructive operation. The description adds valuable behavioral context by disclosing the data source (disclosed M&A transactions) and the return shape, including percentiles and deal counts.

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 compact and front-loaded with the core question, then immediately states what is returned and which inputs are accepted. There is no redundant or filler text.

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 two-parameter read-only lookup with no output schema, the description supplies the key return details and accepted input forms. The only notable omission is how to discover valid industry identifiers, which is where sibling list_industries would be relevant.

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 coverage is 100%, so the schema already documents both parameters and the size-bracket enum. The description adds concrete accepted-value examples such as 'hvac', 'dental-practice', and 'HVAC', which clarify that ids and plain names are both valid.

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 clearly defines a retrieval tool for industry M&A valuation multiples and specifies the exact metrics returned (EV/EBITDA, EV/Revenue percentiles, deal counts). It distinguishes the resource well but does not explicitly differentiate its role from the sibling list_industries tool, which could be used for industry discovery.

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 by the framing question and the note that results can be narrowed to one enterprise-value size bracket. However, there is no explicit guidance on when to use this tool versus list_industries, nor any stated exclusions or prerequisites.

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

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

The annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is fully covered. The description adds only that the result set is the publisher's covered industries with ids and names; with no output schema it could say more about whether the list is exhaustive or ordered.

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?

A single front-loaded sentence with no filler; the returned fields are parenthesized inline rather than buried in a second sentence. Every fragment earns its place.

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 zero-param list tool with no output schema, the description covers the essentials: what entity is listed and that each entry carries an id and a name. Minor gaps remain (whether the catalog is complete/static, ordering), but nothing blocks a correct call.

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?

The tool takes no parameters (empty object, additionalProperties false), so there is nothing for the description to disambiguate. Baseline 4 applies; no parameter gaps exist by construction.

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+resource ('list the industries and sub-verticals') and adds domain scope: small/mid-market M&A valuation multiples published by ExitValue.ai. An agent can immediately tell it's the enumeration tool versus get_valuation_multiples, though the sibling isn't named explicitly.

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 usage is only implied — the description never says 'call this first to obtain valid industry ids before calling get_valuation_multiples', nor does it state any exclusions. An agent can infer the discovery role from the word 'list', but nothing in the text confirms it.

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