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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?

Annotations already cover readOnly/destructive/idempotent hints, so the bar is lower. The description adds meaningful behavior: it commits credits, auto-classifies tasks, selects the optimal tool sequence, uses appropriate LLM models, and returns specific deliverable types. It also explains delivery methods. 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?

Four sentences with no redundancy. The first sentence is a clear front-loaded purpose, followed by prerequisite guidance, behavioral detail, deliverable examples, and delivery options. 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?

With an output schema present, the description does not need to explain return values in depth. It covers the prerequisite (rigor_plan), execution behavior, deliverable examples, and delivery modes, which is adequate for a tool with nested parameters and multiple options.

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 a baseline of 3 applies. The description does not add syntax or format details beyond the schema, though it does mention delivery methods (SSE, webhook, polling) which map to the delivery parameter. Overall, the schema handles parameter semantics sufficiently.

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 'Execute a structured workflow end-to-end,' which is a specific verb and resource. It further explains it classifies tasks, selects tool sequence, and returns complete deliverables, listing examples. It clearly distinguishes from rigor_plan by instructing to call that first for preview.

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 tells users to call rigor_plan first to preview steps and costs before committing credits, which is clear when-to-use guidance. It does not mention when not to use or detail alternatives like rigor_status, but the main alternative is addressed.

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 (readOnly, idempotent, non-destructive), the description adds critical behavioral context: it returns a plan without executing anything and explicitly mentions 'Free — no credits charged.' This helps the agent understand the tool's side-effect-free nature and cost implications, which annotations alone do not convey.

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 long, front-loaded with the core purpose, and every sentence adds value (plan generation, classification/framework selection, and no-execution/cost clarification). There is zero 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 (nested preferences, optional task_type) and the existence of an output schema, the description adequately conveys the tool's role and non-executing nature. It could mention the optional task_type override, but the schema already covers that, so the description is sufficiently complete for an agent to select and invoke 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 schema provides 100% coverage for all parameters, including detailed descriptions for task_description, task_type, and preferences. The description adds no additional parameter-level semantics beyond what the schema already documents, so it meets the baseline but does not exceed it.

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 what the tool does: it provides a structured workflow plan with cost estimates, classifies the task, and selects framework sequences. It also explicitly says it returns a plan without executing anything, distinguishing it from execution-oriented siblings like rigor_execute.

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 opens with 'Before executing a complex task,' giving clear contextual guidance on when to use this tool. However, it does not explicitly name alternative tools or state when not to use it, relying on the sibling tool list for contrast.

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 annotations already mark this as read-only, idempotent, and non-destructive. The description adds that the tool returns progress, step results, and the final deliverable, and that it is intended for polling. This complements the annotations without contradicting them.

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, first states purpose, second gives usage timing. No wasted words.

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 an output schema present and only one parameter, the description covers the tool's role in the workflow lifecycle. It mentions the key return types and the required usage sequence, making it self-sufficient 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?

The single parameter workflow_id is fully described in the schema with format wr_xxx and origin. The description reinforces that the ID comes from rigor_execute, adding workflow context beyond the schema's basic 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 the tool checks the status of a Rigor workflow and enumerates what it returns (progress, step results, full deliverable). This distinguishes it from siblings like rigor_execute (which starts workflows) and rigor_plan (which plans 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?

It explicitly instructs to use after rigor_execute with polling delivery, giving a clear usage context. However, it does not explicitly state when not to use or mention alternatives, so it falls just short of full 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=true, idempotentHint=true, and destructiveHint=false, establishing the safety profile. The description adds behavioral context by noting the tool returns 'status, progress, capacity usage, and available actions per workflow,' which goes beyond the annotation. It also implies that pagination is used for large result sets. No contradictions.

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 with no filler. The first sentence front-loads the core action and scope, and the second sentence explains practical use cases. 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?

Given the presence of a rich output schema and comprehensive parameter documentation, the description covers the tool's purpose, capabilities, and real-world use cases sufficiently. It explains why you'd use this tool (monitoring workflows, capacity usage, identifying stuck/completed workflows) without needing to list return fields or parameter details. The description is complete for a read-only list tool of this complexity.

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 provides 100% coverage with detailed descriptions for all five parameters, including valid statuses and cursor semantics. The description mentions 'filtering and pagination' at a high level but does not add meaning beyond the schema, which already explains each parameter thoroughly. Therefore, a baseline 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 uses a specific verb 'List' with a clear resource 'Rigor workflows' and scope 'for your organization'. It also distinguishes from siblings like rigor_status by indicating it lists all workflows with pagination and filtering, and mentions return fields. This clearly identifies 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 explicitly states the use case: 'monitor workflow state, understand concurrent limit usage, and identify stuck or completed workflows.' This provides clear context for when to use the tool. However, it does not name alternative tools or explicitly state when not to use it, so it doesn't earn a 5.

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