EvalyMe

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

Annotations already establish a safe, idempotent, closed-world read, so the description's job is to add what they cannot. It does: unsupplied figures receive documented neutral defaults surfaced in an `assumptions` field, and yearly growth/annual churn are compounded into monthly rates — both non-obvious behavioral facts an agent cannot infer from the schema.

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, each carrying distinct information: what it computes, what it accepts, how gaps are handled, and the one-listing scope limit. Outputs are front-loaded and there is no restatement of the title or schema.

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?

An output schema exists, so return values need not be detailed, yet the definition still previews them. For a 12-parameter tool with full schema coverage and annotations covering safety, the remaining risk — silent defaults and unit conversion — is explicitly addressed.

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 baseline is 3, but the description adds meaning the schema lacks: that missing figures are defaulted rather than required, and that defaults are reported back. Precedence rules and unit conversions are already in the field descriptions, so no further credit is due there.

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 definition gives a specific verb and resource ('Evaluates a business-for-sale listing') and enumerates the exact outputs produced: valuation range, price position relative to it, and price multiples of ARR and profit. An agent knows precisely what this returns before opening the schema.

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 clarifies the input context (figures a listing states, sourced from Acquire.com, Flippa, or a broker) and constrains scope with 'Evaluates one listing per call.' It does not name the siblings `value_business` or `explain_methodology`, so the boundary between listing analysis and general valuation is left implicit.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds that the content is a fixed published reference including a versioning policy, which hints at stability, but adds little beyond that. No return-structure or freshness detail beyond the content list.

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, front-loaded with the verb and resource before the content enumeration. The inline list is long but each item tells the agent something about the returned material rather than restating the title.

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?

With no output schema and no parameters, the description must carry the burden of describing returns, and it does so by enumerating the formula, multiples, adjustments, weights, archetype rules, limitations and versioning. An agent knows both what it gets and why it would call it.

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?

Zero parameters, so there is no parameter semantics to document and the baseline applies. Nothing in the description conflicts with the empty object schema.

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?

Specific verb ('Returns') plus a specific resource ('EvalyMe's published valuation methodology'), then an enumeration of exactly what that resource contains. It is trivially distinguishable from analyze_listing and value_business, which perform valuations rather than describe the method behind them.

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?

'For explaining or citing how an EvalyMe valuation was produced' gives a clear use context and implies the explanatory/citation scenario rather than the computation scenario. It does not explicitly name the siblings as the alternative for computing a valuation, so the routing is inferable rather than stated.

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?

Goes beyond the annotations by disclosing determinism with an explicit method version (unified-v1), that identical inputs yield identical results, and that out-of-range values return an error naming the field. That error contract and version pinning are genuinely useful and not available from readOnlyHint/idempotentHint. It stops short of describing output shape or whether partial/invalid optional inputs degrade gracefully, but with annotations already covering safety the bar is met.

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?

Three short sentences, front-loaded with what the agent gets back, then applicability, then behavioral guarantees. No filler and no repetition of the title.

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?

An output schema exists so return values need no further explanation, the five required metrics are named, optional inputs are flagged, and the failure mode is stated. Nothing an agent needs in order to call this correctly is missing.

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% with per-field descriptions, ranges, and an enum whose members are individually explained, so the schema does the heavy lifting. The description only enumerates the metric names and flags active users, CAC, and LTV as optional — no formats, units, or thresholds beyond what the schema already states.

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 ('Estimates') plus the exact outputs (valuation range, 0-100 investor score, archetype, top three priorities) and the inputs they derive from. An agent can distinguish this from analyze_listing and explain_methodology without opening any schema.

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 clause 'For a business the user owns or operates' gives a real applicability condition, but there is no when-not guidance and no routing against the siblings (e.g., when to use explain_methodology or analyze_listing instead). Usage is implied rather than prescribed.

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