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

Annotations already indicate read-only, non-destructive, and idempotent behavior. The description adds valuable context beyond annotations by specifying that it's 'Free — no credits charged' (addressing cost implications) and mentioning 'projection' and 'budget alerts' (clarifying output scope). It does not contradict annotations, as 'Get' aligns with readOnlyHint=true.

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 front-loaded with the core purpose in the first clause and efficiently adds context in subsequent phrases. Every sentence ('Get today's tracked LLM spend, per-model breakdown, projection, and budget alerts. Free — no credits charged.') earns its place by providing essential information without 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 complexity (budget tracking with alerts), rich annotations (readOnlyHint, idempotentHint, etc.), and no output schema, the description is mostly complete. It covers purpose, cost, and output scope but could benefit from more detail on return values (e.g., format of breakdown or alerts) since there's no output schema. However, it adequately supports agent usage with the provided context.

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, with the parameter 'daily_limit' fully documented. The description does not add any additional semantic details about parameters beyond what the schema provides (e.g., it doesn't explain how 'daily_limit' affects alerts in more depth). With high schema coverage, 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 clearly states the tool's purpose with specific verbs ('Get', 'tracked', 'projection') and resources ('LLM spend', 'per-model breakdown', 'budget alerts'). It distinguishes itself from siblings like burnrate_estimate, burnrate_optimize, and burnrate_track by focusing on daily tracking and budget alerts rather than estimation, optimization, or general tracking.

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 this tool ('Get today's tracked LLM spend') and includes a cost benefit ('Free — no credits charged'), but it does not explicitly state when not to use it or name specific alternatives among the sibling tools (e.g., burnrate_estimate for projections or burnrate_track for broader tracking).

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 provide idempotentHint=true and destructiveHint=false, indicating safe, repeatable operations. The description adds valuable context beyond annotations by specifying it's for pre-execution estimation and includes optimization suggestions, though it doesn't detail rate limits or authentication needs. No contradiction with annotations 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 front-loaded and concise with two sentences that efficiently convey purpose and output without waste. Every sentence earns its place by stating when to use the tool and what it returns, making it easy to scan and understand.

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 (estimation with optimization suggestions), rich annotations, and full schema coverage, the description is largely complete. However, the lack of an output schema means the description doesn't detail return values (e.g., cost breakdown format), leaving a minor gap in contextual information.

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%, thoroughly documenting the 'plan' parameter and its nested properties. The description adds minimal semantic value beyond the schema, only implying the parameter is for estimation input without providing additional syntax or format details, meeting the baseline for high schema coverage.

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 with specific verbs ('estimate the total LLM cost') and resource ('multi-step agent plan'), distinguishing it from siblings like burnrate_budget, burnrate_optimize, and burnrate_track by focusing on pre-execution estimation rather than budgeting, optimization, or tracking.

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 this tool ('Before executing a multi-step agent plan') and what it provides ('per-step breakdown and optimization suggestions'), clearly differentiating it from alternatives such as burnrate_optimize (which might focus on optimization without estimation) or burnrate_track (which likely tracks costs during/after execution).

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 cover key traits: readOnlyHint=false (mutation), idempotentHint=true (safe to retry), destructiveHint=false (non-destructive). The description adds value by clarifying the optimization method ('model substitution') and optional budget fitting, but doesn't disclose rate limits, auth needs, or detailed behavioral impacts beyond 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 front-loaded with the core purpose in the first sentence, followed by an optional feature. Both sentences earn their place by clarifying functionality without redundancy, making it efficient 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?

Given the tool's moderate complexity (optimization with model substitution), rich annotations, and full schema coverage, the description is mostly complete. It lacks output schema details (e.g., what the optimized plan looks like), but annotations and context provide sufficient guidance for an agent to use it effectively.

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 schema fully documents parameters. The description adds minimal semantics: 'plan' is for optimization via substitution, and 'target_budget' is optional for cost checking. This aligns with the schema but doesn't provide significant extra meaning, warranting the baseline 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's purpose: 'Get a cheaper equivalent plan via model substitution' specifies the verb ('get'), resource ('cheaper equivalent plan'), and method ('model substitution'). It distinguishes from siblings like burnrate_estimate (which estimates costs) and burnrate_budget (which likely sets budgets) by focusing on optimization through substitution.

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: use this tool to find cheaper alternatives via model substitution, optionally with a budget check. It implies usage after cost estimation (burnrate_estimate) but doesn't explicitly state when-not-to-use or name specific alternatives among siblings, keeping it at a 4.

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 context beyond annotations: it clarifies that cost is computed server-side and the tool is free with no credits charged. Annotations provide hints (e.g., idempotentHint: true, readOnlyHint: false), but the description enhances understanding by explaining the cost aspect and free nature, which isn't covered by annotations. No contradiction with annotations is present.

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

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

The description is extremely concise and front-loaded: three short sentences that efficiently convey purpose, cost computation, and free nature. Every sentence adds value without redundancy, making it easy for an agent to quickly grasp the tool's essence.

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 (6 parameters, no output schema), the description is reasonably complete. It covers the core purpose, cost handling, and pricing, which complements the well-documented input schema and annotations. However, it lacks details on return values or error cases, which could be useful since there's no output schema, leaving some gaps in full contextual understanding.

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 schema fully documents all 6 parameters (e.g., provider, model, input_tokens). The description doesn't add any parameter-specific details beyond what's in the schema, such as explaining relationships between parameters or usage nuances. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.

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: 'record actual token usage' after an LLM call. It specifies the action (record) and resource (token usage) with context (after LLM call). However, it doesn't explicitly differentiate from siblings like burnrate_estimate or burnrate_budget, which likely serve different functions in the burnrate tracking system.

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 context ('After an LLM call') and mentions it's 'Free — no credits charged,' which provides some guidance. However, it doesn't explicitly state when to use this tool versus alternatives like burnrate_estimate (for estimation) or burnrate_budget (for budgeting), nor does it mention any exclusions or prerequisites beyond the implied post-LLM-call timing.

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 context beyond annotations: it explains that the tool 'Returns cached result on hit, saving one LLM call', which clarifies the performance benefit and caching behavior. Annotations provide hints (readOnlyHint=false, openWorldHint=true, idempotentHint=true, destructiveHint=false), but the description enhances this by detailing the outcome (cached results) and purpose (avoiding duplicate LLM calls). No contradiction with annotations is present.

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

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

The description is concise and front-loaded, consisting of two sentences that directly state the purpose and benefit. Every sentence earns its place: the first explains what the tool does, and the second highlights the value (saving LLM calls). No unnecessary details or redundancy are 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?

Given the tool's complexity (checking for duplicates with semantic matching) and rich annotations (readOnlyHint=false, openWorldHint=true, etc.), the description is mostly complete. It covers the core functionality and behavioral outcome. However, without an output schema, it doesn't detail return values (e.g., structure of cached results), leaving a minor gap. The annotations help compensate, making it sufficient but not exhaustive.

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 schema fully documents all parameters. The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain 'content' further or provide examples). However, it implicitly references parameters by mentioning 'hashed and embedded for matching', which aligns with the schema but doesn't offer new semantics. Baseline score of 3 is appropriate given high schema coverage.

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 with specific verbs ('check if an identical or semantically similar task has already been completed') and resource ('LLM task'), and distinguishes it from siblings by focusing on duplicate checking rather than budget tracking or guardrail management. It explicitly mentions returning cached results on hit, which differentiates it from execution tools.

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 with 'Before executing any LLM task', indicating when to use this tool. It also implies alternatives by suggesting it 'saving one LLM call', which hints at using this instead of directly executing tasks when duplicates might exist. The context of sibling tools (like dedupq_complete) further clarifies its role in a workflow.

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 context beyond annotations: it explains the caching mechanism (hash and embedding for future matching) and the purpose of storing results. Annotations cover idempotency and non-destructive behavior, but the description complements this by detailing the tool's role in a deduplication workflow. 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, efficient sentence that front-loads the key action ('store the result') and explains the purpose without waste. Every word contributes to understanding the tool's role in caching.

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 complexity (caching with hash/embedding), annotations provide good coverage (idempotent, non-destructive), and the description explains the caching mechanism. However, without an output schema, it doesn't describe return values or potential errors, leaving a minor gap. Overall, it's mostly complete for a caching tool.

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

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

Schema description coverage is 100%, so the schema fully documents parameters. The description mentions 'content' and 'result' but doesn't add meaning beyond the schema's descriptions. It implies parameter usage (e.g., content for hash/embedding) but doesn't provide additional syntax or format details. Baseline 3 is appropriate given high schema coverage.

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 with a specific verb ('store') and resource ('result'), and distinguishes it from its sibling dedupq_check by focusing on caching completed tasks rather than checking for duplicates. It explains the caching mechanism for future identical or similar tasks.

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 this tool: 'After executing a task, store the result so future identical or similar tasks return a cache hit.' It provides clear context for usage (post-task execution) and implies an alternative (dedupq_check for pre-task checking), though it doesn't explicitly name alternatives.

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

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

Annotations provide hints (idempotent, non-destructive, etc.), but the description adds valuable behavioral context: it specifies that the evaluation is deterministic (no LLM involvement) and returns a binary outcome (allow or deny). This goes beyond annotations by clarifying the decision-making process and output format, though it doesn't detail rate limits or auth needs.

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 front-loaded and concise, with two sentences that efficiently convey the tool's purpose, behavior, and outcome. Every sentence adds value without redundancy, making it easy to understand quickly.

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 complexity (policy evaluation with deterministic rules), annotations cover safety aspects, and the description adds key behavioral details. However, without an output schema, the description only mentions 'returns allow or deny' but doesn't elaborate on the response structure or potential error cases. It's mostly complete but could benefit from more output context.

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 schema already documents both parameters thoroughly. The description doesn't add meaning beyond what the schema provides (e.g., it doesn't explain how 'agent_id' or 'proposed_action' are used in evaluation). Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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 with specific verbs ('evaluate', 'returns') and resources ('proposed agent action', 'governance policies'), and distinguishes it from siblings by focusing on policy evaluation rather than budget tracking, deduplication, or other functions. It explicitly mentions the deterministic rule evaluation approach, which further clarifies its 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 implies usage context by specifying it's for evaluating proposed agent actions against policies, but it doesn't explicitly state when to use this tool versus alternatives like 'guardrail_create_policy' or 'guardrail_list_policies'. It provides clear guidance on its function but lacks explicit comparisons or 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?

The description adds valuable behavioral context beyond annotations: it specifies that names must be unique per org (a constraint not in annotations) and that the operation is free with no credits charged (cost implication). Annotations cover basic hints (e.g., readOnlyHint: false, destructiveHint: false), but the description enhances this with practical details. No contradiction with annotations 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 extremely concise with two sentences that are front-loaded and waste no words. The first sentence states the core action, and the second adds key constraints and cost information. Every sentence earns its place by providing essential information.

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 complexity (creation with 5 parameters, no output schema), the description is somewhat complete but has gaps. It covers uniqueness and cost, but lacks details on what a governance policy does, how it's used, or what happens after creation. Annotations provide basic hints, but more behavioral context (e.g., error handling, typical use cases) would improve completeness.

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 schema fully documents all 5 parameters. The description does not add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain 'rules' or 'priority' further). Baseline is 3 as the schema handles parameter documentation adequately.

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 verb 'Create' and resource 'governance policy', making the purpose explicit. It distinguishes from sibling tools like 'guardrail_list_policies' by focusing on creation rather than listing. However, it doesn't specify what a 'governance policy' governs in this context, leaving some ambiguity about the domain.

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 no guidance on when to use this tool versus alternatives like 'guardrail_check' or other sibling tools. It mentions that names must be unique per org, which is a constraint but not usage guidance. There's no indication of prerequisites, typical scenarios, or comparisons to other tools.

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 provide read-only, non-destructive, and idempotent hints, which the description doesn't contradict. The description adds valuable context: it specifies 'active' policies (not all policies), mentions ordering by priority, and notes no credit cost, which are behavioral traits not covered by annotations. However, it doesn't detail output format or pagination.

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 front-loaded with the core purpose in the first clause, followed by ordering detail and cost note. Both sentences earn their place by adding clarity and practical information without waste, making it highly efficient 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?

Given the tool's simplicity (0 parameters, no output schema) and rich annotations, the description is mostly complete. It covers purpose, ordering, and cost, but lacks details on output format (e.g., what data is returned) which could be useful despite annotations. For a list tool, this 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?

With 0 parameters and 100% schema description coverage, the baseline is 4. The description doesn't need to explain parameters, and it appropriately avoids redundant information, focusing instead on the tool's purpose and behavior.

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 ('List active governance policies') and resource ('governance policies'), with additional detail about ordering ('ordered by priority'). It distinguishes from siblings like 'guardrail_create_policy' by focusing on listing rather than creation, but doesn't explicitly contrast with 'guardrail_check' which might also involve policies.

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 when needing to see active policies, especially with priority ordering, but lacks explicit guidance on when to use this versus alternatives like 'guardrail_check' or 'guardrail_create_policy'. The 'Free — no credits charged' note hints at cost considerations but doesn't define specific scenarios or 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?

Annotations provide hints (readOnlyHint=false, openWorldHint=true, idempotentHint=true, destructiveHint=false), but the description adds valuable context beyond this: it specifies the tool is for 'checking' failure patterns, which aligns with non-destructive behavior, and mentions the return format (severity, suggestions, confidence), giving insight into output behavior not covered by annotations. No contradiction with annotations is present.

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

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

The description is front-loaded with the core purpose in the first sentence and efficiently details the return values in the second, with no wasted words. Every sentence adds value, making it appropriately sized and well-structured for quick comprehension.

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 complexity (3 parameters, nested objects, no output schema) and rich annotations, the description is mostly complete: it explains the purpose, usage context, and return format. However, it could benefit from more detail on behavioral aspects like error handling or rate limits, which are not covered by annotations or the description, leaving 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?

Schema description coverage is 100%, so the schema already documents all parameters (task_type, filters, task_description) thoroughly. The description does not add meaning beyond the schema, such as explaining how parameters interact or providing usage examples, but it implies the tool uses 'task_type' for categorization and optional inputs for filtering, which is consistent with the schema. Baseline 3 is appropriate given high schema coverage.

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 with specific verbs ('Check for known failure patterns') and resources ('before executing a task type'), and distinguishes it from siblings like 'pitfalldb_report' or 'pitfalldb_stats' by focusing on querying rather than reporting or statistical analysis. It explicitly mentions what it returns ('pitfalls with severity, fix suggestions, and confidence scores'), making the purpose highly specific and differentiated.

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

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

The description provides clear context for usage ('before executing a task type'), implying it should be used proactively to identify potential issues. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., 'guardrail_check' might be a related tool), so it lacks full exclusion or comparative 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?

The description adds valuable behavioral context beyond what annotations provide: it discloses that data is 'PII-scrubbed before storage' (a privacy/security behavior), mentions 'Linked to existing pitfalls if similar' (a system behavior), and states 'Free — no credits charged' (a cost implication). While annotations cover idempotency and non-destructive nature, the description adds meaningful operational details that help an agent understand what happens when invoking this tool.

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

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

The description is perfectly concise at 3 short sentences with zero wasted words. Each sentence adds distinct value: the core purpose, data handling behavior, and cost implication. The structure is front-loaded with the primary function, followed by important operational details.

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 reporting tool with good annotations (idempotent, non-destructive, open world) and complete schema coverage, the description provides adequate context. It covers the key behavioral aspects (PII scrubbing, linking to existing data, no cost) that an agent needs to understand. The main gap is the lack of output schema, but the description compensates somewhat by indicating what happens to the data after submission.

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

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

With 100% schema description coverage, the input schema already provides complete parameter documentation. The description doesn't add any specific parameter semantics beyond what's in the schema - it doesn't explain the structure of the 'failure' object or provide examples of valid 'task_type' values. The baseline of 3 is appropriate since the schema does the heavy lifting for parameter documentation.

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 specific action ('Report an agent failure') and resource (the pitfalldb system), distinguishing it from sibling tools like 'pitfalldb_query' or 'pitfalldb_stats' which query or analyze data rather than submit reports. The description explicitly mentions PII-scrubbing and linking to existing pitfalls, providing additional context about what the tool does beyond just the name.

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 this tool: when an agent failure occurs that needs to be reported. It mentions 'Linked to existing pitfalls if similar' which implies this tool is for reporting new failures that may connect to known issues. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, though the context suggests it's for reporting rather than querying existing data.

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 provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds valuable context beyond annotations by specifying that it's 'Free — no credits charged', which informs about cost implications not captured in annotations, though it doesn't detail rate limits or authentication needs.

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 front-loaded with the core purpose in the first phrase, followed by specific data points and a cost note. Every sentence earns its place by adding clarity or useful context without redundancy, making it efficient 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?

Given the tool's low complexity (0 parameters, read-only, idempotent) and rich annotations, the description is complete enough for agent use. It covers purpose, key data points, and cost, though without an output schema, it doesn't detail return values, which is a minor gap but acceptable given the annotations provide safety context.

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 0 parameters with 100% description coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, maintaining focus on the tool's purpose and behavior, which aligns with the baseline for zero parameters.

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 specific verb 'Get' and the resource 'aggregate statistics', listing concrete data points like total pitfalls, total reports, top task types, and recent activity. It distinguishes from sibling tools like 'pitfalldb_query' and 'pitfalldb_report' by focusing on aggregated metrics rather than querying or reporting individual records.

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 for retrieving statistical overviews but does not explicitly state when to use this tool versus alternatives like 'pitfalldb_query' for detailed queries or 'qualitygate_trends' for trend analysis. It mentions 'Free — no credits charged', which provides some cost context but lacks explicit guidance on scenarios or 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?

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds value by disclosing 'Free — no credits charged,' which is a behavioral trait (cost implications) not covered by annotations. It does not contradict annotations, as 'Get' aligns with read-only operations.

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 front-loaded with the core purpose in the first sentence and adds a useful behavioral note in the second. It is highly concise with zero wasted words, efficiently conveying key information without unnecessary elaboration.

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 complexity is moderate (2 parameters, no output schema), the description provides a clear purpose and cost insight. Annotations cover safety and idempotency well, but the lack of an output schema means the description could benefit from more details on return values or insights. It is mostly complete but has a minor gap in output expectations.

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, with clear documentation for both parameters ('days' and 'check_type'). The description does not add any parameter-specific details beyond what the schema provides, such as examples or usage tips. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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: 'Get daily pass/warn/fail counts and insights for validation history.' It specifies the verb ('Get') and resource ('validation history'), but does not explicitly differentiate it from its sibling 'qualitygate_validate', which might handle validation differently. The description is specific but lacks sibling distinction.

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 some context with 'Free — no credits charged,' implying cost considerations, but does not specify when to use this tool versus alternatives like 'qualitygate_validate' or other sibling tools. Usage is implied through the mention of validation history, but no explicit guidance on when or when not to use it is given.

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 context beyond what annotations provide. While annotations indicate non-destructive, idempotent operations with open-world hints, the description reveals this is a validation tool with both deterministic (regex, JSON schema, syntax) and optional LLM-powered analysis capabilities. It doesn't mention rate limits or authentication needs, but provides useful operational context.

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

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

The description is perfectly concise and front-loaded in a single sentence that immediately communicates the core functionality. Every word earns its place, with no redundant information or unnecessary elaboration.

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 complexity (7 parameters including nested objects) and lack of output schema, the description provides good context about what the tool does. However, it doesn't explain what the validation results look like or how to interpret them, which would be helpful since there's no output schema. The annotations help with behavioral understanding, but result interpretation remains unclear.

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

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

With 100% schema description coverage, the input schema already documents all 7 parameters thoroughly. The description mentions 'deterministic checks (regex, JSON schema, syntax) plus optional LLM-powered tone and factual analysis' which maps to the check_types parameter, but doesn't add significant semantic value beyond what's already in the schema descriptions.

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 with specific verbs ('validate agent output against your rules') and resources ('deterministic checks plus optional LLM-powered tone and factual analysis'). It distinguishes itself from sibling tools like guardrail_check or dedupq_check by focusing specifically on output validation with both deterministic and LLM-powered analysis.

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 about when to use this tool ('validate agent output against your rules') and what types of checks it performs. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools (e.g., guardrail_check might be for different types of validation).

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