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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context: 'Free — no credits charged' clarifies cost implications, and 'today's' scopes the operation. It does not contradict 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?

Two short sentences deliver all core information without filler. The first sentence leads with the primary action and output, and the second adds cost context. 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?

The tool is simple (1 optional param, output schema exists), and the description covers its purpose and scope. However, it does not clarify why one would choose this over the closely related burnrate_track, leaving a minor completeness gap.

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 optional daily_limit parameter, which fully explains the parameter's meaning. The description itself does not add parameter details, but the schema carries the burden, 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 clearly states the tool retrieves today's tracked LLM spend, per-model breakdown, projection, and budget alerts. The verb 'get' plus the resource (budget info) is specific and differentiates it from siblings like burnrate_track or burnrate_estimate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by mentioning budget alerts and spend tracking, but it does not explicitly state when to use this tool versus alternatives like burnrate_track or burnrate_optimize. No exclusion criteria or alternative references are provided.

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?

Adds a non-annotation behavioral trait: 'Costs 1 credit', which is not in the annotations. Also describes the output ('per-step breakdown and optimization suggestions'), adding context beyond the idempotentHint and destructiveHint annotations. No contradiction with 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?

Three sentences, front-loaded with the primary purpose. Each sentence adds distinct value: purpose, return value, and conditional alternative with cost. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, the description adequately covers the use case, output, and costs. The schema and output schema handle parameter and result details, so the description is sufficient. Could be more complete with e.g. explanation of 'credit', but not necessary.

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 covers 100% of parameters with descriptions, so the description does not need to add parameter-level detail. The description only refers to the 'multi-step agent plan' conceptually, without explaining parameter syntax or semantics beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'estimate the total LLM cost' for a 'multi-step agent plan' before execution. It also mentions the return of a per-step breakdown and optimization suggestions, distinguishing it from a generic estimation 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?

Explicitly states when to use: 'Before executing a multi-step agent plan'. Provides an explicit alternative: 'If the estimate exceeds your budget, pipe the same plan into burnrate_optimize'. This gives clear context and a condition for switching to a sibling tool.

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?

Beyond annotations (idempotentHint=true, destructiveHint=false), the description discloses that the tool costs 1 credit, returns an optimized plan with specific components, and implies a non-mutating operation ('Returns the optimized plan'). This adds useful context without contradicting 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 description is compact and front-loaded with purpose. Each sentence adds essential information: what, when, return value, optional parameter, and cost. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers the tool's function, usage context, inputs, outputs, and side effects (credit cost). The description is especially complete given the schema annotations and output schema presence, leaving minimal ambiguity for an 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 already has 100% coverage, but description enhances meaning by stating the plan parameter uses 'Same schema as burnrate_estimate' and explains target_budget's role ('constrain the optimization'). This goes beyond simple field definitions.

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 opens with a specific action and result: 'Get a cheaper equivalent plan by substituting models with lower-cost alternatives.' This clearly distinguishes the tool from siblings like burnrate_estimate and burnrate_track.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage condition: 'Call after burnrate_estimate if the estimated cost exceeds your budget.' Also gives optional parameter guidance ('Optionally set target_budget to constrain the optimization') and references the predecessor tool.

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 idempotentHint=true and destructiveHint=false. The description adds valuable context beyond these: 'Free — no credits charged' and 'Returns the recorded cost entry with computed margin versus the prior estimate,' disclosing both cost implications and return behavior. This is a good supplement to 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?

Three sentences, each serving a distinct purpose: action, usage guidance, and return behavior. Information is front-loaded, concise, and free of fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity, the description covers the essential aspects: what it does, when to use it, return value, and cost. The output schema exists, so return details are further specified. No significant gaps remain for an agent to select and 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 coverage is 100% with detailed descriptions for all parameters. The description adds meaning by mentioning 'this model and token range' when computing margins, linking the parameters (model, input_tokens, output_tokens) to the tool's estimation logic. This provides extra context 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?

The description clearly states the action with a specific verb and resource: 'Log the actual cost of an LLM call after execution.' It distinguishes from sibling tools like burnrate_estimate by positioning this as recording actuals for calibration, and the title 'Record actual LLM spend' reinforces the purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage timing: 'Call this after every LLM request to build calibration data.' It references burnrate_estimate as the beneficiary of the data, implying an alternative use case (estimating vs. recording). However, it does not explicitly state when NOT to use the tool or name alternatives directly.

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?

The description discloses the hit/miss behavior, the cost of 1 credit, and the complementary dedupq_complete flow, adding value beyond the annotations (readOnlyHint=false, idempotentHint=true). It does not mention potential side effects on the cache or error conditions, but those are not critical given the simple check purpose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each conveying distinct information: purpose, hit/miss behavior, and cost. No unnecessary words or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the core workflow (check, execute, complete), mentions cost, and relies on the output schema for return values. It is adequate for a tool with 4 parameters and 100% schema coverage, though it could mention edge cases like cache misses on similar content or stale 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 description coverage is 100%, so the baseline is 3. The description does not add significant parameter semantics beyond what the schema already provides, such as the hashing behavior for content, which is already in the schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool checks for identical or semantically similar tasks before executing LLM tasks, and differentiates from dedupq_complete by explaining that on a miss, the user should execute the task and call dedupq_complete to cache the result.

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 explicitly says 'Before executing any LLM task, check...' providing clear when-to-use. It also gives an alternative—calling dedupq_complete after a miss—but does not mention explicit conditions when not to use the tool, so it falls short of fully explicit when-not 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?

Annotations already declare readOnlyHint=false (write operation), idempotentHint=true, and destructiveHint=false. The description adds the credit cost (2 credits) and explains the cache mechanism, providing useful behavioral context beyond annotations. No contradiction with 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 description is a single, front-loaded sentence that conveys purpose, mechanism, and cost without wasted words. Every clause earns its place, making it concise and well-structured.

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 cache store tool with full schema and an output schema, the description covers the essential purpose, cost, and relationship to dedupq_check. However, the workflow order (e.g., calling after a cache miss) is only implied, not explicitly stated, which is a minor gap.

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 schema description coverage is 100%, with each parameter (content, result, task_id, hash_only) already well-described. The description does not add significant meaning beyond mapping 'content' to original task content and 'result' to the stored result. Baseline 3 is appropriate because the schema handles parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action: store the task result after execution, with a specific purpose (future cache hits via dedupq_check). It distinguishes this tool from siblings by naming the complementary dedupq_check tool. The verb 'store' combined with the resource 'result' gives unambiguous 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?

The description provides clear context for when to use: after executing a task, to enable future cache hits. It references dedupq_check as the corresponding lookup tool, implying a usage workflow. However, it does not explicitly state when not to use the tool or compare it to alternatives beyond the companion tool.

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?

The description adds valuable behavioral details beyond annotations: it is deterministic, uses no LLM, and costs 1 credit. It also states the return type (allow/deny with reason). The annotation idempotentHint and destructiveHint are consistent, and no contradiction exists.

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 concise—four sentences, each adding information: purpose, return value, prerequisite, and behavioral traits. It is front-loaded with the core function and avoids fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists and annotations are present, the description covers necessary context: what the tool does, what it requires, and its cost/determinism. It is complete enough for an agent to decide whether and how to invoke it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with detailed descriptions and an example for proposed_action, so the description need not repeat parameter semantics. The only param-related addition is the prerequisite mention, which applies to the overall call rather than to a specific 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 description clearly states the tool evaluates a proposed agent action against governance policies, using a specific verb ('Evaluate') and resource ('your governance policies'). It distinguishes itself from siblings by focusing on policy-based allow/deny, unlike dedupq_check or qualitygate_validate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides an essential prerequisite: at least one active policy created via guardrail_create_policy, which tells the agent when the tool is usable. It does not explicitly name alternatives or exclusion cases, but the context implies this is the policy-checking tool.

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?

The description goes beyond the minimal annotations by disclosing persistence ('Policies persist until explicitly deleted'), error behavior ('Duplicate policy names return an error'), and return value ('Returns the created policy with its ID and active status'). These details add significant context beyond the annotations, which only provide basic hints. However, it doesn't mention potential authorization requirements or the interaction of multiple policies, so it's not fully comprehensive.

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 uses six short, informative sentences with no redundancy. It is front-loaded with the primary purpose, followed by usage, persistence, error handling, and return value. Every sentence earns its place, and there is no padding or unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has an output schema, so return value details are covered separately. The description explains prerequisite ('Call this before using guardrail_check'), persistence, error condition, and return behavior. It lacks specifics on how multiple policies are evaluated together, but given the priority parameter in the schema and the companion guardrail_check tool, this is a minor gap. Overall, the description provides enough context for correct usage.

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%, meaning all parameters have descriptions. The description adds a high-level summary of rule syntax ('Define rules using and/or/not operators over action types, resource patterns, and budget thresholds'), but the schema already provides detailed examples for each parameter. Thus the description adds marginal value beyond the schema, warranting the baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Create a persistent governance policy' with a specific verb and resource. It explicitly explains that guardrail_check evaluates the policy on subsequent calls, distinguishing it from sibling tools like guardrail_check itself, which presumably evaluates policies. The tool's role as a policy creator 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?

The description provides explicit usage guidance: 'Call this before using guardrail_check — checks require at least one active policy.' This directly tells the agent when to use this tool relative to its sibling, and the note about duplicate policy names returning an error also informs the agent to avoid name collisions. This is clear and actionable.

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?

The description discloses the credit cost ('Costs 2 credits'), a behavioral trait not present in annotations. It also summarizes the return contents (severity, fix suggestions, confidence scores). This adds meaningful context beyond the annotations, which already indicate read-only and idempotent behavior, but the cost is an important non-obvious effect.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the primary purpose, and includes the return format, cost, and a pointer to the sibling reporting tool. Every sentence contributes novel information with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With a rich input schema, an output schema, and annotations, the description sufficiently covers the tool's behavior. It specifies when to use it, what it returns, and the associated cost, leaving no critical gaps for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for parameters, so the baseline is 3. The description does not elaborate on parameter usage beyond what the schema already provides; the only hint is the notion of 'task type' which is a schema field. No additional semantic clarification is offered.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Check for known failure patterns before executing a task type.' It also specifies the output ('pitfalls with severity, fix suggestions, and confidence scores'), which distinguishes it from sibling tools like pitfalldb_report. This is a specific verb-resource pair with clear scope.

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 explicitly advises using the tool 'before executing a task type,' providing clear usage context. It also suggests the complement: 'After your agent runs, submit failures via pitfalldb_report.' While it does not mention all alternative tools, it gives a concrete usage pattern and a related action.

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?

Beyond the annotations, the description adds valuable behavioral context: PII scrubbing before storage, linking to existing pitfalls, and the fact that it's free. These disclose important privacy and deduplication behaviors that are not stated in annotations, enriching the agent's understanding of side effects.

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 exceptionally concise: four short sentences, each adding distinct information (action, privacy, deduplication, cost). No fluff or redundancy exists; every sentence 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?

Given the tool's moderate complexity (3 params, nested object) and the presence of output schema and annotations, the description covers the essential behavioral points: purpose, PII handling, linking, and cost. It could mention explicit usage scenarios relative to sibling tools, but the current description is sufficiently complete for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with meaningful descriptions for every parameter, including nested fields. The tool description does not add any parameter-specific details beyond what the schema already provides, so the baseline score of 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 opens with the specific verb-resource pair 'Report an agent failure,' which clearly states the tool's purpose. This distinguishes it from sibling tools like pitfalldb_query, which is for querying, not reporting. The additional context about PII scrubbing and linking further clarifies the tool's function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly indicates this tool is for reporting agent failures, which is a distinct usage context from sibling tools. It does not explicitly mention alternatives or exclusions, but the differentiation from pitfalldb_query is implicit and clear, qualifying it as 'clear context, no exclusions'.

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?

Beyond the annotations, the description discloses the return structure (verdict with pass/warn/fail, 0-100 score, per-check issue details) and variable costs per check type. It also highlights optional LLM-powered tone and factual analysis, giving the agent useful behavioral context. The description does not contradict any 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 description is three sentences, each packed with essential information: when to use, what it does, what it returns, a pointer to a related tool, and cost structure. It is front-loaded and contains no filler or redundant phrasing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, the description covers the core purpose, usage window, return format, and cost, while the output schema handles verdict/score details. It omits discussion of the 'override' parameter and filter directives, but the schema covers those. The presence of output schema and annotations reduces the burden on the description, making it reasonably complete.

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, so the baseline is 3. The description adds some context by grouping checks into deterministic vs LLM-powered and mentioning cost implications, but it does not elaborate on specific parameters like 'override' or 'directives'. The schema itself carries the semantic weight.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool validates agent output against user-defined rules, using deterministic checks and optional LLM analysis. It explicitly distinguishes itself from related tools like qualitygate_trends, which is for spotting recurring failure patterns. The verb 'validate' plus the resource 'agent output' makes the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear timing guidance ('After your agent generates output, validate it ... before shipping') and an explicit alternative for trend analysis. However, it does not mention when *not* to use this tool or compare it with other validation-like sibling tools, so it lacks exclusionary 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?

Goes well beyond the annotations by disclosing that execution commits credits, classifies and routes work to models, supports three delivery mechanisms, and can return validated JSON. The nested preferences.execution description adds fallback and exclusion behavior (require_approval, attachments, prior_workflow_id), which is valuable side-effect 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?

Every sentence earns its place: the first sentence is the core action, the second sequences the plan step, the third clarifies internal routing/deliverable, and the fourth gives the atomic-task fast path. It is dense 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?

For a complex, stateful tool, the definition covers prerequisites, cost, delivery methods, output shape options, and the task-type vocabulary. Because the output schema exists and schema descriptions are complete, an agent has enough to invoke both the standard and direct paths 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?

Even with 100% schema coverage, the description adds real decision value: it explains what preferences.execution='direct' and preferences.output_contract actually do (single-call validation, cheapest-model routing, quality-review skip) and provides the task_type taxonomy with atomic vs multi-step categories. This is more than restating 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?

Begins with a specific verb and object—'Execute a structured workflow end-to-end'—and immediately distinguishes itself from rigor_plan by instructing the agent to call rigor_plan first for preview/cost. The deliverable classes (solution designs, competitive analyses, governance documents) make the tool's scope concrete.

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?

States exactly when to use it: after rigor_plan, before committing credits. It also gives decision rules for mode selection—use direct execution + output_contract for atomic work such as classification, scoring, and extraction, with the default/standard path otherwise.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds meaningful behavioral traits beyond those: it does not execute the task, it classifies and selects the framework sequence, the response includes allowed_modes, and it is free ('no credits charged'). These are useful, non-obvious disclosures.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, front-loaded with the core action, then classification, no-execution behavior, response hint (allowed_modes), and pricing. Every sentence earns its place and no filler is present.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is complex, but the input schema is extremely rich (100% coverage), annotations cover the safety profile, and an output schema exists. The description covers the remaining essentials: what it returns (plan with cost estimates), side-effect-free execution, free pricing, and the allowed_modes hint. Nothing critical 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 100%, with very rich per-parameter documentation (task_type vocabulary, execution preferences, output_contract structure). The description itself adds no parameter-level semantics, so the baseline of 3 applies per the rubric.

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 opens with a specific verb and resource: 'get a structured workflow plan with per-step cost estimates.' It names the behavior (classifies task, selects framework sequence) and explicitly states it returns a plan 'without executing anything,' which clearly differentiates it from the sibling rigor_execute. This is unambiguous and actionable.

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 gives clear context: use this 'before executing a complex task,' and mentions allowed_modes to determine execution eligibility, implying the next step is rigor_execute. However, it does not explicitly name alternatives or exclusions, such as 'use rigor_execute directly for atomic tasks' — that nuance lives only in the schema.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is known. The description adds meaningful behavior beyond that by explaining the return payload: progress, step results, and the full deliverable when complete. This helps the agent understand what the response will look like over the lifecycle of the workflow.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the primary action and followed by the key usage instruction. Every word adds value, and it is appropriately sized for a single-parameter status 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?

Given the simple one-parameter schema, existing read-only/idempotent annotations, and an output schema, the description covers the essential points: purpose, return content, and timing relative to rigor_execute. It does not mention failing workflows or error handling, but the output schema likely covers those states, so it is nearly complete.

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 schema already documents the only parameter (workflow_id) with format and source ('returned by rigor_execute (format: wr_xxx)'), so schema coverage is 100%. The description adds no new parameter-level insights but also does not contradict the schema, so the baseline score 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 uses a specific verb+resource ('Check the status of a running or completed Rigor workflow') and clearly distinguishes it from siblings like rigor_execute and rigor_plan by emphasizing it is the status retrieval tool. It also mentions the key output (progress, step results, full deliverable), making the purpose 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?

The description explicitly states when to use the tool: 'Use after rigor_execute with polling delivery to retrieve results.' This gives clear invocation timing relative to a sibling tool. It does not mention exclusions or alternative use cases for the other workflow tools, but the primary integration context is well-defined.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description goes beyond annotations by disclosing that it lists and searches workflows, returns per-workflow status/progress/capacity usage/available actions, and supports filtering/pagination. It also reveals the tool surfaces data that could be used to infer concurrency limits and find prior work, which is useful context. It does not claim any side effects and does not contradict 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 description is a compact paragraph that front-loads the core function, then lists returned data, then gives use cases, then a sibling-routing tip. Every sentence contributes either scope, use-case, or navigation guidance. There is no repetition of schema details and no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is a read-only list/search operation with an output schema, full parameter docs, and strong annotations. The description covers why the tool exists, what it returns, and how to route to rigor_status. There is no missing information needed for an agent to select and invoke it safely and 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 coverage is 100%, so all nine parameters are already documented well, especially q which has a detailed substring-matching explanation. The description adds meaning by framing the q parameter's practical purpose (find prior work on a subject before commissioning it again) and framing the tool's overall filtering/pagination behavior. It also highlights status as a filter relevant to identifying stuck workflows, which complements the schema's parameter list.

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 (list/search), a specific resource (Rigor workflows), and enumerates the returned fields (status, progress, capacity usage, available actions) plus pagination and filtering. It clearly distinguishes itself from siblings like rigor_status by positioning itself as the workflow-level discovery tool and telling the agent to pair a q hit with rigor_status for deliverable reads, and differs from rigor_execute/rigor_plan which create/run workflows.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states explicit use cases: monitor workflow state, understand concurrent limit usage, identify stuck/completed workflows, and find prior work via q before commissioning it again. It gives a concrete routing rule: pair a q hit with rigor_status to read the deliverable. It does not explicitly say when not to use it, but the openWorldHint:false and the clear scope make the guidance strong and actionable.

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