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

With no annotations provided, the description carries the full burden. The verb 'Returns' implies a read-only operation, and the description is clear about what is returned. However, it does not disclose additional behavioral traits such as response size, structure, or performance implications, which is a moderate gap.

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 consists of two concise, front-loaded sentences. The first sentence clearly states the action and resource, and the second adds a usage hint. Every word earns its place, with no redundancy or fluff.

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 parameters and no output schema, so the description is the primary source of information. It lists all returned components and provides a clear use case. It could be more explicit about the return format, but it is sufficiently complete for an agent to decide to invoke 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?

The tool has zero parameters, so the description is not required to explain parameter semantics. Per the rubric, this baseline is 4, and the description does not introduce any misleading parameter information.

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 starts with a specific verb 'Returns' and names a clear resource: 'the complete creator-deals library'. It lists all the components (pricing models, usage terms, scope checklist, negotiation moves, sponsor vetting), which distinguishes it from sibling tools that handle each component individually.

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 description states 'Useful for full agent context,' which provides clear guidance on when to use this tool—when comprehensive context is needed. It does not explicitly mention alternatives or exclusions, but the sibling tools are implied as targeted alternatives.

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?

With no annotations provided, the description carries the burden of explaining behavior. It discloses that each result includes the move, the shape of what to say, and what never to do, which is useful context. However, it does not mention any safety profile, side effects, or return format beyond this 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?

The description is two sentences, front-loaded with the purpose and followed by a concise list of situations and output components. Every word earns its place with no redundancy or filler.

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 simple lookup tool with one enum parameter and no output schema, the description is fairly complete: it states what the tool returns, the valid inputs, and the composition of the output. It lacks edge-case guidance (e.g., what if situation is invalid) but that is adequately handled by the schema's enum constraint.

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 single parameter situation has an enum, and the description lists the allowed values, which repeats the schema. It adds meaning about the output (move, shape, never-do) but does not clarify how to select a situation or what each value implies beyond its name. Schema coverage is 0%, so the description only partially compensates.

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 states the tool returns a negotiation move for a given situation, with a specific verb ('Returns') and resource ('negotiation move'). It lists the six possible situations, which distinguishes it from sibling tools like interpret_pricing_model or interpret_usage_term.

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 description implies usage—call it when you have one of the enumerated situations—but does not explicitly say when to use this tool versus alternatives. It provides no exclusions or comparisons with sibling tools, leaving the agent to infer based on the situation names.

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?

With no annotations provided, the description carries the burden. It transparently states the tool 'Returns' a checklist, which implies a safe read operation. The name and verb leave no ambiguity about behavior, though it does not add extra context like output format or side effects (which are minimal for a getter).

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 core purpose, and the enumeration of all 10 checklist items is compact and informative. Every word 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 no-parameter, no-output-schema tool, the description is fully self-contained. It states exactly what is returned, lists the contents, and gives a clear usage context. There is no missing information needed for an agent to decide to invoke it correctly.

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 has zero parameters, so the baseline score is 4. The description does not need to explain parameters and does not, leaving no confusion.

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 states the tool returns a 10-point deliverable scoping checklist and enumerates the specific points. This distinguishes it from sibling tools like interpret_pricing_model or get_negotiation_move, which serve different purposes.

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 description provides explicit timing guidance: 'Run it before quoting or signing anything.' It does not mention explicit exclusions or alternatives, but the context is clear and distinct from sibling tools.

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?

With no annotations, the description takes on the full burden. It discloses unique behavioral traits: returns a favorability assessment, the key rule, and a counter pattern, and notably states 'No absolute rates — anchor to your own base,' which is a critical usage caveat. It does not describe any side effects or hidden limitations, but for a read-only interpret tool, this is sufficient.

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 two sentences long, front-loads the action verb, and every clause adds meaningful information. There is zero filler or redundant phrasing.

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 single-parameter tool with no output schema, the description is fully complete: it specifies the input domain, the exact type of output (three components), and the crucial behavioral constraint about absolute rates. An agent can invoke this tool correctly without further clarification.

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 input schema has 0% description coverage, so the description must compensate. It does by listing all enum values (flat_fee, cpm_based, performance_based, per_click_sponsorship, retainer) and explaining that the parameter is the pricing model to interpret. It also adds the semantic context that results are relative to the user's own base, which goes beyond the raw 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?

The description clearly states the action ('Interpret a pricing model') and the specific resource (pricing model in a creator deal), listing all accepted model types. It also distinguishes the tool from siblings like interpret_usage_term by focusing specifically on pricing models and describing concrete outputs (when it favors you, key rule, counter pattern).

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 description implies when to use the tool: whenever you have one of the listed pricing model types in a creator deal and need an interpretation. It does not explicitly exclude alternatives or mention sibling tools, but the context is clear enough for an agent to select it over other interpret tools. Lack of explicit 'vs. alternatives' wording prevents a 5.

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?

No annotations are provided, so the description carries the full burden. It describes what the tool returns (meaning, relative value vs base rate, trap to watch), which is helpful behavioral information. Yet it does not mention limitations, edge cases, or that it is a read-only operation.

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 purpose, and no wasted words. It efficiently conveys the tool's function, input scope, and output.

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 simple tool with one enum parameter and no output schema, the description covers the key aspects: what it does, what inputs it accepts, and the general nature of its output. It could be more detailed about the output format, but it is sufficient for most use cases.

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 0%, so the description must compensate. It lists the term enum values, giving context that these are the acceptable inputs, but it does not explain each term's meaning. This partially compensates but is not exhaustive.

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 uses a specific verb ('Interpret') with a clear resource ('usage/rights term') and lists concrete examples. It distinguishes itself from sibling tools like interpret_pricing_model by focusing on usage/rights terms rather than pricing models.

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?

Provides clear context: for briefs or contracts, and enumerates the specific terms it handles. However, it does not explicitly state when not to use it or mention alternatives, so it misses the full 'when/when-not/alternatives' threshold.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the tool returns checklists and mentions the overpayment-refund scam pattern, but it does not explicitly confirm that the operation is read-only or describe what the checklist contents include beyond the scam reference. However, the 'returns' wording implies a non-mutating action.

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 a single sentence that is front-loaded with the primary action ('Returns green/yellow/red flag checklists') and then adds relevant context. Every phrase earns its place, with no filler or redundancy.

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?

Given the tool's simplicity (no parameters, no output schema, no annotations), the description sufficiently covers what the tool does and what to expect. It could be slightly more complete by fleshing out the types of flags included, but the mention of the overpayment-refund scam pattern provides a concrete example that anchors the tool's purpose.

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 has zero parameters, so there is no parameter schema for the description to augment. The baseline for such cases is 4, and the description adds value by explaining the purpose of the returned checklists, which indirectly clarifies that no inputs are required.

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 uses a specific verb ('returns') and resource ('green/yellow/red flag checklists') to clearly state what the tool does. It also specifies the target ('vetting an inbound sponsorship or brand-deal offer') and distinguishes itself from siblings by focusing on offer vetting and scam patterns.

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 description clearly indicates when to use this tool: when an inbound sponsorship or brand-deal offer needs vetting. It does not explicitly name alternatives or exclusions, but the context is sufficiently defined for an agent to select it over sibling tools like interpret_pricing_model or get_negotiation_move.

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