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

Beyond the annotations (readOnly, idempotent, openWorld=false, destructive=false), the description discloses that it is pure arithmetic with no I/O and no external calls, no auth or rate limits, that results are rounded to 2 decimals, and what the response contains. It also specifies failure behavior: an unknown method or a missing method-required parameter returns an error rather than a value.

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?

It is a single dense block but front-loads purpose first, then routing, then parameter mapping, then behavior, so information is discoverable in priority order. Minor waste exists in enumerating the return fields (value, method, inputs, assumptions, chapter, formula_number, calculation steps) when an output schema already exists.

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 15-parameter, conditionally-required tool with an output schema, the description covers purpose, alternative routing, method-to-parameter requirements, unit conventions, side-effect profile, rounding, and error behavior. An agent has everything needed to select the method and assemble the correct parameter set.

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%, so the baseline is 3, but the description adds conditional semantics the flat schema cannot express: which parameters each method requires (e.g. ev_ebitda needs enterprise_value + ebitda), that only `method` is required, that non-applicable parameters should be omitted, and the unit convention for fractions. Some of the fraction/unit detail (0.10 = 10%) already appears in the schema, so it is not fully additive.

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 analytic resource (market multiples from comparables: P/E, P/S, EV/EBITDA, EV/Revenue, regression-adjusted) and states that the `method` parameter selects which ratio is computed. It also routes the agent away from this tool for pre-revenue/private startups by naming valuation_core, so the boundary against at least the key sibling is explicit.

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?

"Use when public comparables exist; for pre-revenue or private startups use valuation_core" gives both the inclusion condition and the exclusion condition with the alternative tool named. Nothing about when to pick this over the other valuation_* siblings is left to inference for the common case.

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