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

No annotations are provided, so the description carries the full disclosure burden. It does reveal one non-obvious trait: empty inputs yield a template rather than an error. It says nothing about what the scoring output looks like, how mismatched array lengths are handled, or whether weights are validated — gaps that matter for a computation-only tool.

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 tight sentences with the core mechanic front-loaded and the invocation shortcut second. No filler or restatement of the tool name.

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?

Four parameters, no annotations, and no output schema means the description must stand in for a lot. It covers the input contract loosely but never indicates the return value (scores per option, ranking, winner) or what the 'template' fallback actually produces, leaving the agent guessing about results.

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 50%: weights and question are documented inline, while options and criteria are not. The description compensates by implying options/criteria are parallel arrays and can be omitted for a template, but it leaves ambiguous whether 'equal-length' means options, criteria and weights must all match, which the schema only states for weights-vs-criteria.

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 and resource ('Score options against weighted criteria') and the goal ('pick objectively'), so the mechanics are unambiguous. It does not explicitly contrast itself with the sibling frameworks (swot_analysis, lean_canvas, marketing_mix), which would be needed for a 5.

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 'Pass equal-length arrays or leave blank for template' tells the agent how to invoke it in two modes, which is real usage guidance. However, it never says when this framework is the right choice versus swot_analysis or lean_canvas, so the selection guidance is only implied.

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 carries the full disclosure burden, and it does disclose one meaningful trait: the tool fills in blocks the caller omits. It says nothing about what the output looks like, whether generated content is inferred rather than derived from input, or how it behaves if all nine blocks are supplied.

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 tight sentences with no filler, and the constraint that matters for invocation ('provide any 2+ blocks') arrives immediately.

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 10-parameter generative tool with no annotations and no output schema, this is far too thin — the shape of the returned canvas, the handling of omitted blocks, and per-block formatting are all unaddressed.

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 only 10% — only 'name' is documented — but the nine remaining property names are standard Lean Canvas block terms that are largely self-explanatory to a knowledgeable agent. The description still omits format expectations (prose vs bullets, length) and does not reconcile 'provide any 2+ blocks' with the schema requiring only 'name'.

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 the resource precisely ('One-page business model (9 blocks)') and implies a generate/complete action, so the agent knows this produces a lean canvas. It does not state an explicit verb and never names the sibling frameworks it must be distinguished from, so differentiation is only implicit.

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?

'Provide any 2+ blocks; the template guides completion of the rest' gives a concrete input precondition, which is real usage guidance. However, it never says when to pick this over swot_analysis, marketing_mix, or decision_matrix, so the selection decision is left entirely to inference.

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 disclosure burden. It does not state whether the tool creates or persists anything, whether the inputs are merely validated or used to generate output, or what the caller gets back — meaningful gaps for a tool named marketing_mix with no safety hints attached.

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 single sentence is front-loaded and free of padding, which is good. But its brevity comes at the cost of under-specification — the phrase reads more like a label than a usable instruction, so the economy doesn't fully earn 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?

With 6 parameters at 0% schema coverage, no annotations, and no output schema, the description would need to do substantial work. Instead it covers only four of the six inputs and gives no indication of what the tool returns or how the inputs are consumed, leaving the agent materially under-informed.

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% across 6 parameters, so the description must compensate and it only partially does. It maps Product, Price, Place, and Promotion to the 4Ps, but leaves product_offer and target_customer entirely unexplained and doesn't clarify that the 'pitch line' corresponds to product_offer, nor what format any field expects.

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 the framework and enumerates its components (Product, Price, Place, Promotion, pitch line), which tells an agent roughly what domain this tool covers. However, it never supplies a verb — it doesn't say whether the tool builds, evaluates, or stores a 4P plan — and it doesn't explicitly distinguish itself from framework siblings like swot_analysis or lean_canvas beyond the label.

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?

There is no guidance on when to reach for marketing_mix versus swot_analysis, lean_canvas, or decision_matrix, and no prerequisites or exclusions are stated. The agent must infer from the framework name alone that this is the marketing-planning option.

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 exist, so the description carries the full burden. It does disclose a useful behavioral trait the schema cannot — that output includes 3-5 offensive and defensive strategic actions — but it never says whether the four category arrays are inputs the caller must supply or dimensions the tool infers, nor whether they are optional. That ambiguity is material for a 5-param tool.

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 the framework named first and the value-add outcome second. Nothing is wasted and nothing important is buried.

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 annotations, no output schema, and 20% schema coverage across 5 parameters, the description should explain return shape and the input semantics of the four arrays. It gives partial credit by promising derived strategic actions but leaves the input contract and output format substantially under-specified.

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 only 20% — only 'subject' is documented, and the description adds nothing about it. The four arrays (strengths, weaknesses, opportunities, threats) are named as analysis dimensions but the description never clarifies whether to populate them or omit them, which is the central parameter decision for this tool.

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 gives a specific verb-and-resource ('Run a SWOT analysis: strengths, weaknesses, opportunities, threats') and adds the differentiating outcome ('derive 3-5 offensive and defensive strategic actions'). It does not name the sibling frameworks (marketing_mix, lean_canvas, decision_matrix), so the agent must already know the SWOT concept to distinguish it, but the purpose itself is unambiguous.

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?

There is no guidance on when to use this versus marketing_mix, lean_canvas, or decision_matrix, and no stated prerequisites or exclusions. Usage is only implied by the framework name, leaving the selection decision entirely to inference.

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