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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions checking 'connection status and configuration' but doesn't describe what this entails (e.g., whether it performs a live test, returns cached data, requires authentication, or has rate limits). For a diagnostic tool with zero annotation coverage, this is a significant gap.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse 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 simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate but lacks depth. It doesn't explain what the check returns (e.g., success/failure, details on configuration) or behavioral aspects, which could be important for a status-checking tool. However, the low complexity means it's not severely incomplete.

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

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

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics, but with no parameters, the baseline is 4 as it adequately handles the absence of inputs without unnecessary detail.

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 ('Check') and resource ('Feedbucket API connection status and configuration'), making it immediately understandable. However, it doesn't differentiate from sibling tools (all feedback-related), which are unrelated to API status checking, so it doesn't need sibling differentiation but could mention this is the only API diagnostic 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?

The description provides no guidance on when to use this tool versus alternatives or in what context. It implies usage for checking API status but doesn't specify scenarios like troubleshooting, pre-operation verification, or monitoring, leaving the agent to infer usage.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool adds a comment but doesn't mention permissions required, whether this is a mutating operation, rate limits, or what happens to the feedback item after commenting. For a tool that modifies data with zero annotation coverage, this is insufficient behavioral context.

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

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

The description is a single, efficient sentence that states the action, target, and purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality.

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 mutation tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like side effects, error conditions, or return values, leaving significant gaps for an agent to understand how 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 already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain feedback_id format or comment length limits). Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the action ('Add a comment') and target resource ('to a specific feedback item'), with purpose ('to ask for clarification or provide updates'). It doesn't explicitly distinguish from sibling tools like feedback_resolve, which also modifies feedback items, but the purpose is specific enough to understand its 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?

No guidance is provided on when to use this tool versus alternatives like feedback_resolve (which might resolve feedback) or other feedback-related tools. The description mentions the purpose but doesn't specify prerequisites, constraints, or comparative usage scenarios with sibling 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?

With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses that the response includes a 'resource' field with screenshot URLs for certain feedback types, and provides specific instructions about downloading and viewing images to see visual context (green dot showing user clicks). This goes beyond basic read operation disclosure.

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 appropriately sized and front-loaded with the core purpose, followed by important behavioral details. The second sentence could be slightly more concise, but overall it's efficient with zero wasted sentences.

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

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

For a single-parameter read tool with no output schema, the description provides good completeness: it explains what data is returned (comments, attachments, session data), special handling for screenshots, and why the visual context matters. It could mention response format or error cases, but covers the essential context well.

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% (the single parameter 'feedback_id' is fully described in the schema), so the baseline is 3. The description doesn't add any parameter-specific information beyond what's in the schema, but doesn't need to given the complete 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 specific action ('Get full details') and resource ('a specific feedback item'), listing the included data types (comments, attachments, session data). It distinguishes from sibling tools like feedback_list (which presumably lists multiple items) and feedback_comment (which focuses on comments).

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 'a specific feedback item' (vs. listing multiple), but doesn't explicitly state when to use this tool versus alternatives like feedback_stats or feedback_resolve. It provides some guidance about screenshot handling but not about tool selection.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool 'automatically optimizes data to prevent overwhelming responses' and mentions screenshot URLs in the 'resource' field, which are useful behavioral insights. However, it doesn't cover important aspects like authentication requirements, rate limits, error conditions, or whether this is a read-only operation (though 'fetch' implies it).

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

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

The description is appropriately sized with two sentences that each earn their place. The first sentence states the core purpose and key features, while the second provides important usage guidance about sibling tools. It's front-loaded with the main functionality and avoids unnecessary 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?

For a tool with 8 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the purpose, filtering capabilities, and relationship to feedback_get, but doesn't address the return format, pagination behavior beyond the offset parameter, error handling, or authentication requirements. The lack of output schema means the description should ideally explain what the response contains.

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%, so the schema already documents all 8 parameters thoroughly. The description adds minimal parameter semantics beyond what's in the schema - it mentions 'intelligent filtering' and 'AI optimization' which relate to the parameters but don't provide additional syntax or format details. The baseline of 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool fetches feedback items with intelligent filtering for AI consumption, distinguishing it from sibling tools like feedback_get (which retrieves full details) and feedback_stats (which provides statistics). However, it doesn't explicitly differentiate from feedback_comment or feedback_resolve beyond the 'list' vs 'comment/resolve' 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 clear context for when to use this tool (fetching all feedback items with filtering) and explicitly mentions using feedback_get for retrieving full details and viewing screenshots. It doesn't specify when NOT to use it or compare with all sibling alternatives like feedback_stats or feedback_comment.

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

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

No annotations are provided, so the description carries full burden. It states the tool marks feedback as resolved, implying a mutation, but lacks details on permissions, side effects (e.g., status changes), or error handling. The phrase 'after actioning it' hints at a workflow but doesn't clarify behavioral traits like idempotency or response format.

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 with zero waste—'Mark a feedback item as resolved after actioning it'—front-loading the core action. Every word earns its place, making it appropriately sized for a simple tool.

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

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

Given no annotations and no output schema, the description is incomplete for a mutation tool. It lacks details on what 'resolved' entails (e.g., status update, timestamp), potential errors, or return values. The context signals (3 params, 100% schema coverage) don't compensate for missing behavioral and output 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%, so the schema fully documents parameters (feedback_id, reporter_name, reporter_email). The description adds no parameter-specific details beyond implying feedback_id is required for resolution. Baseline 3 is appropriate as the schema handles semantics, with no extra value from the 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 verb ('Mark as resolved') and resource ('a feedback item'), specifying the action and target. It distinguishes from siblings like feedback_comment (add comment) and feedback_get (retrieve), though not explicitly. However, it doesn't fully differentiate from all siblings (e.g., feedback_stats might involve resolution status).

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 minimal guidance with 'after actioning it', implying this should be used post-resolution, but offers no explicit when-to-use vs. alternatives like feedback_comment for updates or feedback_list for viewing. No prerequisites or exclusions are mentioned, leaving usage context vague.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates this is a read operation ('get') and describes the output content (statistics breakdowns), but doesn't cover aspects like performance characteristics, error conditions, or data freshness. The description doesn't contradict any annotations, but it's moderately informative given the lack of structured 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, well-structured sentence that efficiently conveys the tool's purpose and output scope. It's front-loaded with the core action ('get a quick summary') and lists key statistics without unnecessary elaboration. However, it could be slightly more concise by avoiding the dash list format, but overall it's highly efficient.

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, no annotations), the description is reasonably complete for a read-only statistical tool. It specifies what statistics are returned, which compensates for the lack of output schema. However, it doesn't address potential behavioral nuances like data scope (e.g., all projects vs. current project) or update frequency, leaving some contextual gaps.

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 tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the absence of inputs. The description adds no parameter-specific information, which is appropriate here. Baseline for 0 parameters is 4, as the description doesn't need to compensate for any gaps in 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 tool's purpose with specific verbs ('get a quick summary') and resources ('project feedback statistics'), including breakdowns by status, type, and recent activity. It distinguishes itself from siblings like feedback_get or feedback_list by focusing on aggregated statistics rather than individual feedback items or lists. However, it doesn't explicitly contrast with all siblings (e.g., api_status).

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 through 'quick summary' and the statistical nature of the output, suggesting it's for overview purposes rather than detailed inspection. However, it lacks explicit guidance on when to use this tool versus alternatives like feedback_list (which might provide raw data) or feedback_get (for specific items), and doesn't mention prerequisites or exclusions.

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