Idea to Product Sequencer

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

Annotations already declare the safety profile (readOnlyHint, idempotentHint, non-destructive, closed-world), so the bar is lower. The description still adds real context beyond them: the two-part return shape (Markdown document plus short JSON summary), the full inventory of document sections, and the conditional rule that with no commitment-level evidence the build start is deferred until after the test decision date.

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 purpose and return format are front-loaded in the first sentence, which is good structure. However, the middle sentence is a sprawling ~90-word comma-separated inventory of document sections that reads as a wall of clauses; it is informative given the missing output schema but could be tightened considerably.

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 26-parameter, nested-object tool with no output schema, the description compensates well on the output side by enumerating exactly what the returned document contains. The remaining gap is the absence of any routing guidance relative to the five sibling tools that cover overlapping sub-steps of the same method.

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 81% with 26 parameters, so the schema does the heavy lifting and a baseline of 3 applies. The description only touches parameter meaning indirectly through the commitment-level-evidence scheduling rule (relevant to `evidence`), adding no syntax or default detail beyond what the schema already documents.

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 and artifact: 'Run the full Idea to Product method on one idea and its feature list and return a Scope and Sequence plan as a Markdown document, followed by a short JSON summary.' The 'full method' framing implicitly distinguishes it from the narrower siblings (cut_mvp, sequence_build, validation_plan), but no sibling is named explicitly, so the routing signal is inferential rather than stated.

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 call this composite tool versus the single-step siblings, nor any prerequisite or exclusion. The one conditional sentence ('When there is no commitment-level evidence yet...') describes internal scheduling behavior, not when an agent should choose this tool over 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?

Annotations declare readOnly/idempotent/non-destructive, and the description adds genuinely useful context beyond them: the classification is rule-based with no AI model, the same input always yields the same answer, risk moves down a fixed evidence ladder, and evidence types (interviews, signups, deposits, payments) affect scoring. It does not disclose size limits, pagination, or latency, but for a pure-compute read tool the behavioral picture is largely covered.

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 purpose clause is correctly front-loaded, and enumerating return contents has value given there is no output schema. But the body is a single run-on sentence that strings nine output categories together with commas, making it hard to scan. A structured list of the return fields would convey the same information more efficiently.

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 complex tool (18 parameters, no output schema) the description carries the return-value burden well, spelling out the classification output, scoring dimensions, red flags, stage, build rules and weekly test, plus the determinism guarantee. What it omits is any statement of expected input shape or how the many optional inputs change the output, but overall it is sufficient to invoke the tool 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?

Schema description coverage is 94%, so the schema already explains nearly every parameter. The description adds only one semantic layer over it: that the evidence parameter feeds the fixed evidence ladder that lowers risk and that billing/price/evidence inputs shape the score. That is a marginal addition on top of an already-complete schema, matching the 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 opening clause gives a specific verb+resource: classify a product or business idea using fixed rules (explicitly no AI model). It goes on to enumerate concrete outputs (10 product types, 9 buyer types, 10 scored assumptions, riskiest assumption, red flags, stage, build rules, weekly test). However, it never contrasts itself with siblings like validation_plan, kill_or_continue or explain_method, so the agent must infer where classification ends and those begin.

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, when-not-to-use, prerequisite, or named alternative anywhere in the description. The closest thing is the implied flow that evidence gathered lowers risk along a ladder, but that describes behavior, not tool selection. An agent gets no help choosing between classify_idea and its six siblings.

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 cover the safety profile (readOnly, idempotent, non-destructive), so the bar is lower, and the description still adds real behavioral value: it describes dependency propagation ('dependencies of a must become must'), packing order, the returned payload, and concrete failure modes ('rejects duplicate ids, unknown dependencies and cycles with the chain named'). That failure disclosure is genuinely useful and not obtainable from the annotations.

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 verb and output are front-loaded, and every clause carries information (rules, propagation, packing, returns, rejection cases) rather than filler. The trade-off is density: it reads as one long comma-spliced sentence, which slightly hurts scanability but not content.

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 complex tool with a nested feature array, 5 parameters, and no output schema, the description is notably complete. It enumerates the return contents (tier, rule, reason, effort totals vs capacity, specific fitting cuts) and the error contract, leaving no obvious gap for an agent to call 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?

Schema description coverage is 80%, so the baseline is 3, but the description adds meaning beyond the schema by explaining how parameters interact: must-have propagation through depends_on, should-haves ordered by 'value per effort point in dependency order', and effort totals measured against capacity. That interpretive layer is not present in 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?

The description names a specific verb and resource ('Sort a feature list into must, should and not now') and the exact rule base (15 rules on tags, manual alternative, buyer demand). It is unambiguous what the tool produces. What it lacks is any explicit differentiation from siblings like build_scope_and_sequence or sequence_build, which an agent might reasonably confuse with a prioritization tool.

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 implied by the input (you have a feature list and a build window, and want an MVP cut), and the described algorithm makes the scenario clear. However, there is no explicit when-to-use statement, no when-not-to-use, and no routing to the alternates among siblings such as build_scope_and_sequence.

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 readOnly, idempotent, non-destructive, non-open-world behavior, so the safety profile is covered. The description adds genuinely new context: this is a static lookup that performs no computation on user data, which tells the agent no user context is required. It also conveys the shape of the return (reference tables with known cardinalities) in the absence of an 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?

Two sentences with no filler; the purpose is front-loaded and the constraint sentence is short and load-bearing. The long enumerated list is verbose but every item maps to a real parameter value, so it earns its space rather than padding.

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 a single enum parameter, complete annotations, and no output schema, the description supplies everything needed: the full set of valid topics with content hints and the behavioral note that no user data is involved. Nothing an agent needs in order to call this correctly is absent.

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 a fully documented enum, so the baseline is 3. The description goes beyond the bare enum labels by describing the content of each topic ('the four stages and their exit gates', 'the 10 product types with their risk priors and build rules'), which helps the agent pick the right value.

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 ('Return one of the method's reference tables') and enumerates exactly which tables exist, so an agent knows the full retrieval surface. The closing clause 'Static reference, no computation on user data' cleanly separates it from computational siblings like classify_idea, cut_mvp, and validation_plan.

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 'Static reference, no computation on user data' implicitly tells the agent to use this when the method definition itself is needed rather than when applying the method to a user's idea. No sibling is named explicitly and there is no explicit when-not-to-use statement, so it stops short of 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?

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar is low, yet the description adds substantial context beyond them: the named decision rules, the deliberate ignoring of sunk money and time, and the full shape of the returned call (score, rule, reason, next action, next review date, what would flip it, portfolio notes).

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?

Purpose and scope are front-loaded, and the rule sentences earn their place because they define the tool's decision logic. It is dense and slightly run-on, and promising '10 named decision rules' while enumerating only five creates a small structural gap.

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, the description usefully describes the return payload, so an agent knows what it gets back, and it discloses the sunk-cost stance. It is complete enough for invocation, though it does not say what happens on malformed or partial product input.

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 67%, and the description adds real meaning by tying inputs to outcomes: metrics (target, actual, kill line, must-hit), evidence, stage, review date, last activity, monthly revenue and cost all map to specific rules. It does not explain the defaults (kill_at, weight, direction) that the schema itself documents.

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+scope: makes a push/iterate/park/kill call on 1-12 products from their metrics and evidence. That is clearly distinct from sibling tools such as build_scope_and_sequence, cut_mvp or sequence_build, which deal with shaping and ordering work rather than ending or continuing a product.

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 implied by the decision-rule content (you run this to judge whether to keep building a product), but the description never says when to reach for this tool versus the sibling planning tools, nor what input conditions make it inappropriate. No explicit alternatives or exclusions are offered.

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 cover the safety profile (readOnly, idempotent, non-destructive), and the description adds real behavioral context beyond them: weekends are skipped, critical path and slack are computed, missing method tasks are injected, and a default success metric is substituted and flagged when absent. It stops short of describing the returned plan's structure or how conflicts are resolved.

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 core action is front-loaded in the opening clause and every later clause adds a distinct scheduling or milestone behavior. It is a single dense run-on sentence with a long enumeration, which is heavier than ideal but not padded.

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 11 parameters, a nested feature object, and no output schema, the description carries the return-value burden and does so by spelling out the plan's contents (dated schedule, critical path, slack, milestones) and the metrics defaults. It is complete enough to invoke correctly; only the ambiguity with build_scope_and_sequence remains unresolved.

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 91%, so the baseline is 3, but the description adds meaning the schema does not: scope is framed as an MVP cut, builders determine parallel scheduling on working days, include_method_tasks names the four injected tasks, and product_type drives a flagged default success metric. That is genuine semantic value on top of well-documented fields.

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 concrete verb and resource ('Turn a feature list with dependencies into a dated build plan') and enumerates the transformations performed, so the agent knows exactly what it produces. However, it never differentiates itself from the close sibling build_scope_and_sequence (or cut_mvp, which its 'applies the MVP cut' step overlaps with), so an agent has no textual basis for choosing between 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?

There is no 'use this when...' guidance and no named alternative, despite a sibling named build_scope_and_sequence that sounds nearly interchangeable and a cut_mvp sibling whose function the description partially absorbs. Usage must be inferred entirely from the verb phrase.

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 readOnly/idempotent/non-destructive, so the safety profile is covered. The description goes beyond them usefully: it discloses the filter dimensions (buyer type, price, audience, reachable buyers, budget, buildability), the ranking criterion (evidence strength vs cost and days), and the full return contract (sample, steps, pass line, kill line, inconclusive rule, decision date, backup test, ruled-out tests with reasons).

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 purpose is front-loaded and the output contract is valuable, but the middle sentence is a very long run-on that crams the whole 18-test library (with 11 examples) into a parenthetical. The test-name enumeration is bloat the agent does not need in order to invoke the tool.

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, so the description must carry the return contract, and it does so thoroughly (sample, steps, pass line, kill line, inconclusive rules, decision date, backup test, ruled-out tests). Combined with 95% schema coverage on 19 parameters and annotations covering safety, nothing essential for correct invocation 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 description coverage is 95%, so the schema already documents each of the 19 parameters, including defaults. The description restates the filter inputs and the riskiest-assumption default without adding syntax or per-parameter meaning beyond the schema, which is exactly the baseline-3 case.

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 concrete verb+resource (pick/plan a validation test from a named library) and specifies the selection logic (cheapest test that can falsify one assumption within days available). It enumerates 11 of the 18 library tests for concreteness. It never differentiates itself from siblings like kill_or_continue or cut_mvp, so it stops short of 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?

Usage is implied rather than stated: the agent can infer it should call this when it needs to choose a validation experiment under day/budget constraints, and the 'Defaults to the riskiest assumption; pass assumption to choose another' line hints at preconditions. But there is no explicit when-to-use/when-not and no routing to or away from sibling tools.

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