Boardroom Bob

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, and it discloses almost nothing: no output format, no indication of what data the input must contain, no limits or prerequisites. "Plot a portfolio" implies a visualization-style output but never confirms or describes it.

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 or redundancy. It is efficient, though the brevity borders on under-specification rather than true conciseness.

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 annotations, the description should tell the agent what to supply (e.g., portfolio entities, market growth, relative share data) and what comes back. A one-line generic sentence leaves both unanswered for a framework that typically needs structured market data.

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% and the single parameter is documented as "Your problem, decision, project, or question to analyze with this framework." The description adds no syntax, format, or content guidance beyond the schema, so the baseline 3 applies.

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 states a specific verb ("Plot") and a well-known named framework (BCG Growth-Share Matrix), which naturally distinguishes it from siblings like run_swot or run_five_forces. It stops short of explaining scope or what the plot actually produces, so it is clear but not fully differentiated in intent.

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 when-to-use guidance and no comparison to the many sibling framework tools (run_swot, run_value_chain, run_scenario_planning, etc.). An agent gets no help deciding when a BCG matrix is the right framework versus an alternative.

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 behavioral burden, and it discloses almost nothing: no output shape, no depth, no indication of what the 'report' contains. The '[Pro]' tag hints at a tier/access restriction, which is the only behavioral signal present.

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 short fragments, front-loaded with the core action; nothing is padded. The brevity borders on under-specification rather than being genuinely efficient, but there is no wasted prose.

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 tool with no annotations and no output schema, the description should compensate by explaining what the report is and when to invoke it. It does not, leaving the agent without the context needed to prefer this over sibling frameworks.

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?

There is a single parameter with 100% schema description coverage, so the schema already explains that 'input' is the problem/decision to analyze. The description adds no syntax, formatting, or example beyond that baseline.

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 verb and deliverable ('Build a competitive intelligence report'), so the general purpose is legible. However, among siblings like run_five_forces, run_swot, and run_value_chain, which are also competitive/market analysis frameworks, it offers no differentiation of scope or angle.

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 indication of when to choose this framework over the many sibling analysis tools, nor any prerequisites or exclusions. The agent is left to guess purely from the name.

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 does not state whether the tool is a pure read-only analysis, whether it has side effects, what shape the analysis output takes, or how much context the 'input' should contain. For a framework tool with zero structured behavioral coverage, this is a significant 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?

A single front-loaded sentence with no filler, which is efficient. It is arguably under-specified rather than verbose, but nothing in it is wasted.

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 one-parameter analysis tool with no output schema, the description explains the method but not the deliverable (e.g., what the rebuilt solution analysis contains) or how input should be framed. Adequate to invoke, but thin for an agent deciding between 19 similar frameworks.

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?

With a single parameter at 100% schema description coverage, the schema already explains that 'input' is the problem/decision/project to analyze. The description adds no format, length, or content guidance beyond that, so baseline 3 is appropriate.

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 two-part verb phrase ('strip a problem to fundamental truths and rebuild a solution') that identifies the first-principles framework clearly. It does not, however, contrast itself with the many other reasoning frameworks in the sibling list (five whys, inversion, second-order thinking), leaving differentiation to the tool name alone.

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 choose this framework over the 18 sibling frameworks, no prerequisites, and no exclusions. The agent must infer applicability from the tool name and the one-line method description.

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 behavioral burden. It conveys only that this is a one-shot analysis run; it says nothing about the shape, depth, or format of the analysis returned, whether the output is deterministic, or any cost/latency characteristics. There is no contradiction, but essentially no behavioral context either.

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 short sentence with the framework name and scope front-loaded; nothing is wasted or padded. It is terse to the point of under-specification, but that is a completeness issue rather than a conciseness defect.

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 and no output schema, the description is the only place an agent could learn what a Five Forces run yields, and it says nothing about the return. Combined with no differentiation from a large sibling family, the definition is thin for an analysis tool whose output format matters for downstream use.

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% and the single 'input' parameter is fully documented in the schema, so the baseline is 3. The description adds a mild narrowing ('an industry or market') that the schema notably widens to 'problem, decision, project, or question', giving slightly more scoping information than the schema alone.

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 verb ('Run') and a specific named framework ('Porter's Five Forces'), scoped to 'an industry or market'. That is enough for an agent to know what the tool does without reading the schema. It lacks explicit differentiation from siblings such as run_competitive_landscape or run_swot, which apply adjacent strategy frameworks.

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 statement of when to pick this tool, when not to, or which sibling to use for an adjacent analysis. The only hint is the implicit scoping to 'an industry or market'. With 18 near-identical 'run_<framework>' siblings, the absence of routing guidance is a real gap.

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 behavioral burden. It hints this is an analytical operation rather than a mutation, but discloses nothing about depth of iteration, whether the run is interactive, expected latency, or what the agent receives back from a framework invocation.

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 or redundancy. It is efficient, though its brevity is partly the source of the definition's other gaps rather than a virtue of precise packing.

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 framework-runner tool with no output schema and no annotations, the description leaves the agent without knowledge of the return shape, the number of 'whys' produced, or how the output should be consumed. Compared to a tool invoked among 18 similar frameworks, this is 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?

There is only one parameter and schema description coverage is 100%, so the schema already explains that 'input' accepts a problem, decision, project, or question. The description adds nothing beyond that, which is the expected baseline when the schema does the heavy lifting.

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 goal-oriented verb ('Drill past symptoms') and an outcome ('to the root cause'), which conveys the intent of root-cause analysis. However, it never names the framework (Five Whys) or its mechanism, and it does not distinguish this from methodologically adjacent siblings like run_first_principles or run_inversion.

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 choose this tool versus the 18 sibling frameworks, no mention of prerequisites, and no exclusions. The agent must infer applicability purely from the metaphor in the sentence.

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 behavioral burden and discloses almost nothing: no output format, no length/depth expectations, no latency or tier constraints beyond an unexplained "[Pro]". An agent cannot predict what a call returns or what "[Pro]" gates.

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 short sentences with the action and the tier marker front-loaded and no filler. It is efficient, though the brevity is partly under-specification rather than tight editing.

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 capstone synthesis tool with no annotations and no output schema, the description is too thin: it never says what "everything" comprises, whether sibling tool outputs feed in, or what the recommendations look like. The relationship to the framework siblings is left entirely to inference.

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% for the single parameter, and the description adds no syntax, format, or scoping detail beyond it. Baseline 3 applies when the schema already documents the parameter.

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 verb+resource ("Synthesize ... into final strategic recommendations") is clear enough to separate it from the single-framework siblings, but "everything" is never defined. Given the sole parameter is the user's own problem statement rather than prior tool outputs, it is unclear whether this synthesizes earlier framework results or just re-analyzes one prompt.

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 when-to-use guidance, no statement of prerequisites (e.g., run other frameworks first), and no mention of how it relates to the 18 sibling framework tools. The "[Pro]" tag is the only usage signal and is left unexplained.

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 behavioral burden. It does not disclose whether the operation is read-only, what the analysis returns, how long it takes, or what the '[Pro]' marker implies (presumably a paid tier or access requirement). A single functional sentence leaves the agent with no behavioral context.

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?

One short sentence plus a tier tag, front-loaded with the action and framework. Nothing is padded, though the brevity comes at the cost of substance rather than from efficiency.

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 framework-invocation tool with no annotations and no output schema, the description should explain what the analysis produces or how to frame the input. It states only that an analysis is 'set up', leaving the agent unable to predict the result or the '[Pro]' access 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?

Schema description coverage is 100% and there is only one parameter, so the schema already fully documents 'input'. The description adds a marginal framing word ('for a decision') but no syntax, format, or content guidance beyond the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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 states a specific verb ('Set up') and resource ('hypothesis-driven analysis for a decision'), so the agent knows the kind of output to expect. However, it offers no differentiation from the many sibling framework tools (run_mece_issue_tree, run_pyramid_principle, run_second_order_thinking), leaving the agent to infer the boundary from the name alone.

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 when-to-use guidance, no statement of exclusions, and no reference to any alternative sibling tool. The agent is given no criteria for choosing this framework over the other seventeen analysis frameworks available.

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 conveys the method's two conceptual steps but says nothing about whether this is a read-only analysis, what form the result takes, latency, or whether the input is stored — none of the behavioral traits an agent needs beyond the framework idea.

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 nine-word sentence, front-loaded with the action and with no filler or repetition. It is tight, though bordering on aphoristic rather than instructive.

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 1-param analysis tool with no annotations, no output schema, and 18 mutually similar siblings, the description should say more: what the deliverable looks like and how it differs from run_pre_mortem or run_second_order_thinking. It leaves the agent under-informed for selection.

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?

There is a single parameter whose schema description ('Your problem, decision, project, or question to analyze with this framework') is fully documented at 100% coverage. The description adds no meaning beyond that, so the baseline of 3 applies.

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 concrete method: 'Map every way this could fail, then avoid those paths' — a clear verb (map) applied to a specific object (failure modes) plus a second step (avoidance). It tells an agent roughly what the tool will do, but it does not distinguish this from the very similar run_pre_mortem sibling among the 18 run_* frameworks.

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?

No when-to-use, when-not-to-use, or alternative is given. With run_pre_mortem in the sibling list being a near-synonym for failure-mapping, the omission of any routing guidance is a real gap — the agent must guess between them from names alone.

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 states the transformation (messy problem -> clean issue tree) but discloses nothing about structure, depth, mutability, or what the response looks like for a tool with no output 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?

One tight sentence with the outcome front-loaded and zero filler. It is well-sized for the tool's simplicity, though there is little structure to speak of.

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 one-input tool the bar is low, but with no output schema the agent gets no sense of the return structure, and no comparative context against 18 sibling frameworks. The description leaves the agent under-equipped to choose and predict.

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?

Single parameter with 100% schema coverage; the schema already documents 'Your problem, decision, project, or question.' The description adds no syntax, format, or length guidance beyond the schema, so baseline 3 applies.

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 ('Break down') + resource ('MECE issue tree') + input scope ('messy business problem'). It distinguishes itself from the many sibling framework tools by naming the exact framework (MECE issue tree vs. SWOT, Five Whys, etc.), though it never states why an agent should pick it over those siblings.

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?

No when-to-use, when-not, or alternative guidance. 'Messy business problem' implies a decomposition task, but with 18 sibling framework tools the description gives the agent nothing to route on beyond the framework name itself.

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 and no output schema, so the description carries the full behavioral burden. Beyond the useful '[Pro]' tier gating hint, it discloses nothing about what the tool returns, whether it is a safe read-only analysis, or any constraints — a significant gap for a framework 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 plus a tier tag with no wasted words. Structure is efficient, though the '[Pro]' marker sits awkwardly at the end rather than being integrated.

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 tool sitting among ~18 near-identical 'run_*' framework siblings with no annotations and no output schema, the description does not explain how this framework differs from or should be chosen over its relatives. It is too thin to fully orient the agent.

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 lone 'input' parameter is fully documented in the schema and the description adds no syntax or format guidance. Per the high-coverage baseline, a 3 is appropriate.

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 ('Design an operating model') and names the framework's intent ('turn strategy into execution'), so the agent knows what this produces. However, it offers no differentiation from the dense set of sibling framework tools like run_value_chain or run_five_forces.

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 phrase 'turn strategy into execution' implies the context in which the framework applies, giving implied usage. But there is no explicit when-to-use versus when-not, and no routing toward or away from the numerous sibling analysis frameworks.

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 behavioral burden, yet it discloses nothing about what the tool returns, how the framework is applied, or what output shape to expect. The only signal is the vague analytical intent of surfacing trade-offs.

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 short sentence with zero filler, so there is no waste. However, it is under-specified rather than well-structured: the sentence is a slogan and is not front-loaded with what the tool actually does.

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 one-parameter framework tool with no annotations and no output schema, the description is far too thin to guide selection or invocation among many near-identical siblings. It omits the framework's method, inputs beyond the schema, and expected output.

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 'input' parameter has 100% schema description coverage, which already explains that it takes the problem or decision to analyze. The description adds no syntax, format, or content guidance beyond the schema, so the baseline of 3 applies.

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 statement 'Make the invisible trade-offs of a yes fully visible' is a value proposition, not a statement of what the tool does. It never names a verb+resource (analyze/evaluate a decision's opportunity cost) and offers no way to distinguish it from sibling frameworks like run_regret_minimization or run_inversion.

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 indication of when to choose this framework over the other nineteen run_* strategy tools. No trigger conditions, prerequisites, or exclusions are given; an agent must guess based on the tool name alone.

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 must carry the full burden of behavioral disclosure. It does not state that this is a read-only, non-destructive analysis, nor does it describe the output format, whether it returns a list of causes, or any interaction model. The only behavioral hint is that it is a cognitive exercise, which is implied but not made explicit.

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, front-loaded sentence with no wasted words. It is appropriately sized for a simple framework tool, though its terseness leaves little structure beyond the core idea. Every word earns its place, but it could benefit from slightly more explicit framing.

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 annotations, the description should explain what the tool returns and how to interpret the result. Instead, it only conveys the core metaphor, leaving significant gaps about output format, depth of analysis, and usage context. For a framework tool in a crowded sibling set, this is incomplete.

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 100% description coverage for the single 'input' parameter, clearly explaining that it should be the problem, decision, project, or question to analyze. The description itself adds no further parameter information, so the baseline of 3 is appropriate when the schema already does the heavy lifting.

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 vivid language ('Imagine the project failed... and find what killed it') to convey the pre-mortem technique, which is a specific and recognizable analysis method. It distinguishes itself from sibling frameworks like run_inversion or run_five_whys by focusing on prospective failure. However, it does not explicitly state a verb like 'analyze' or 'run', relying on metaphor, which slightly reduces directness.

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 offers no guidance on when to use this tool versus the many other framework siblings (e.g., run_scenario_planning, run_second_order_thinking). It simply describes the technique without indicating appropriate contexts, prerequisites, or alternatives. The agent must infer usage entirely from the name and the one-sentence metaphor.

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 behavioral burden. It implies a non-destructive transformation but never states that nothing is persisted or modified, nor describes the shape, format, or length of what comes back. For an analysis tool with zero structured disclosure, this is a meaningful 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?

A single front-loaded sentence with zero filler; the action and the resulting artifact are both stated immediately. Nothing needs trimming.

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 annotations, the description should explain what an agent gets back (a structured narrative? bullet hierarchy? report?) and any input expectations. It says nothing about the return shape, leaving the agent unable to anticipate or chain the result.

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% and the single `input` parameter is already well documented in the schema ("Your problem, decision, project, or question to analyze with this framework"). The description adds no additional semantics beyond that, so the baseline 3 applies.

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 ("Structure") and resource ("raw data") plus the target form ("top-down pyramid narrative"), which is clearly the Pyramid Principle framework. It does not explicitly differentiate itself from the many sibling frameworks (MECE issue tree, first principles, etc.), so an agent must infer the distinction from the framework name alone.

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 when-to-use guidance, no when-not-to-use guidance, and no mention of any alternative. Given ~19 sibling frameworks that all accept the same kind of free-text input, the agent has no signal for choosing pyramid_principle over, say, mece_issue_tree or so_what.

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 behavioral burden. It only states a perspective and omits what the tool actually does with the input, what kind of output to expect, whether it is read-only, or any limitations. This leaves the agent guessing about the tool's behavior.

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 one short, front-loaded sentence with no wasted words. It is appropriately sized as a hook, though its extreme brevity contributes to gaps elsewhere.

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?

There is no output schema and no annotations, and the description does not explain what to expect from the analysis or when to choose this framework. For a tool competing with 18 siblings in a decision-analysis suite, this is under-specified for correct selection and invocation.

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% for the single 'input' parameter, which is already fully described as 'Your problem, decision, project, or question to analyze with this framework.' The description adds no further parameter meaning, so the baseline of 3 applies when the schema does the heavy lifting.

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 core lens ('80-year-old self's vantage point') and, together with the tool name, makes the regret-minimization framework identifiable. It is clear enough to distinguish from siblings like run_pre_mortem or run_inversion, but it lacks a concrete verb+resource phrase such as 'analyze a decision using regret minimization.'

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?

No guidance is given on when to use this framework versus alternatives. With 18 sibling frameworks available, the description does not state context, exclusions, or conditions that would select this tool over run_inversion, run_second_order_thinking, or others.

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 behavioral burden and delivers almost nothing: no indication of what the session returns (scenario narratives, matrix, probabilities), runtime, or cost. The '[Pro]' marker does imply an access/tier requirement, which is the one piece of behavioral information present.

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 short sentences with the action front-loaded; nothing is wasted or buried. However, the brevity reflects under-specification rather than disciplined editing, which caps it below 5.

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 framework-execution tool with no output schema and no annotations, the description should at minimum describe what the session produces and what the '[Pro]' restriction implies. Neither is covered, leaving the agent to infer the entire behavior contract from the name alone.

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?

Only one parameter exists and schema description coverage is 100%, so the schema already explains 'input' as the problem or decision to analyze. The description adds no format, length, or content expectations beyond that, making the baseline 3 appropriate.

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 ('Run a scenario planning session') plus a scope qualifier ('for multiple futures'), which separates it from sibling frameworks like run_pre_mortem or run_second_order_thinking by naming the scenario-planning method itself. It stops short of explicitly contrasting with those siblings, so it is clear but not maximally differentiated.

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 statement of when to choose scenario planning over the other 18 run_* frameworks, nor any prerequisite or exclusion guidance. The only signal is the '[Pro]' tag, which hints at a tier gate rather than usage guidance.

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 burden. It discloses the analytical depth ('three layers deep'), which is useful behavioral context, but says nothing about whether this is a pure reasoning pass, whether the input is treated as read-only, or how exhaustively the mapping is explored.

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 zero filler; the verb and depth constraint both land immediately. It may be slightly too terse for a framework in a crowded sibling set, but 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 one-parameter reasoning tool with no output schema, the description is minimally adequate but leaves open what the returned mapping looks like (layers, branches, tradeoffs) and when a caller should prefer another framework. An agent can invoke it, but not confidently compare it to alternatives.

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% and the single parameter's schema description already enumerates acceptable inputs (problem, decision, project, question). The description adds only the implication that a decision is the preferred input, which is marginal beyond the 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?

States a specific verb ('Map') and resource ('consequences of a decision') plus scope ('three layers deep'), which lets an agent distinguish it from a simple pros/cons list. However, it does not explicitly contrast with the many sibling frameworks (pre_mortem, inversion, scenario_planning) that also project downstream effects, so sibling differentiation is left to inference.

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 only implied: a decision with non-obvious downstream effects is the natural trigger. There is no explicit when-to-use, when-not-to-use, or routing hint relative to the 18 sibling frameworks, so the agent must guess the boundary conditions.

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 full behavioral burden and discloses almost nothing: no indication of what the analysis returns, whether it is read-only or costs, or what the '[Pro]' tag implies operationally. The '[Pro]' marker is the sole behavioral hint and is unexplained, so an agent cannot tell whether the tool is gated or restricted.

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 short sentence that front-loads the action and framework name, with no wasted clauses. The trailing '[Pro]' token is cryptic and slightly muddies an otherwise clean statement.

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 free-text analysis tool with no annotations and no output schema, the description should at least indicate what the agent gets back (a critique, a list of probing questions, a verdict) and when this framework is the right pick. Neither is present, leaving the agent to guess at the result shape and the selection criteria.

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% for the single 'input' parameter, so the schema already explains that it accepts the problem, decision, project, or question to analyze. The description adds no further syntax, format, or length guidance, so the baseline 3 applies.

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 states a specific verb ('stress-test') and object ('recommendations') tied to a named methodology, the 'So What?' framework, so the agent knows what operation it invokes. However, it does nothing to distinguish this framework from the 18 sibling framework tools (run_five_whys, run_first_principles, etc.), which all follow the same verb+framework 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?

There is no indication of when to choose this framework over run_swot, run_pre_mortem, or any other sibling. The only implicit guidance is that it targets 'recommendations', which is a weak signal against 19 similarly-shaped 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?

No annotations are provided, so the description carries the full disclosure burden, and it discloses almost nothing: no indication of output shape, depth of analysis, whether it produces a four-quadrant structure, or any limitations. 'Rigorous, actionable' is marketing language, not behavioral information.

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 short, front-loaded sentence with no padding or repetition. It is efficient, though the two adjectival qualifiers ('rigorous, actionable') do little work.

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 and no output schema, the description must explain enough for an agent to call this correctly, and it does not: it says nothing about the expected output, the structure of the analysis, or how the free-text input should be framed. For a framework-invocation tool this is a substantial gap, though the simplicity of a one-param tool keeps it from being a total failure.

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% for the single 'input' parameter, which already explains that it is the problem, decision, project, or question to analyze. The description adds no syntax, framing, or format guidance beyond the schema, so the baseline 3 for a fully-documented parameter set applies.

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 clear verb+resource ('Build ... SWOT'), and the tool name itself identifies the framework, so an agent can tell it apart from siblings like run_five_forces or run_bcg_matrix by name. However, the description adds nothing to differentiate the SWOT framework's role or scope versus the other 18 framework tools, and 'rigorous, actionable' are vague qualifiers rather than scope information.

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 statement of when to use this framework versus alternatives such as run_competitive_landscape or run_mece_issue_tree, no prerequisites, and no exclusions. The entire burden of deciding when SWOT is the right framework falls on the agent with zero guidance.

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 and delivers almost nothing beyond the purpose statement. The '[Pro]' tag hints at tier gating but is unexplained, and there is no disclosure of whether the call is read-only, whether it consumes quota, or what form the analysis returns.

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 short clauses with zero filler, and the core action is front-loaded. Nothing is redundant and 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 single free-text framework runner with no output schema, the description is minimally adequate: it conveys the lens being applied. It omits any indication of the shape of the result (e.g. structured breakdown vs. narrative) and leaves the '[Pro]' qualifier unexplained, which is a real gap for an agent deciding whether it can call this tool.

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?

With one parameter at 100% schema description coverage, the schema already explains that 'input' is the problem or question to analyze. The description adds no format, length, or content guidance beyond that, so the baseline 3 applies.

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 ('Map the full value chain') plus a concrete outcome ('find where value leaks'), which clearly separates it from framework siblings like run_swot or run_five_forces. However, it never names or contrasts with any alternative, so an agent must infer the distinction from the framework label alone.

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 when-to-use guidance, no statement of the problem types this framework suits, and no mention of when a sibling such as run_operating_model or run_competitive_landscape would be the better pick. The agent is left to guess from the tool name.

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