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

No annotations are provided, so the description carries full burden. It mentions 'analyze its impact' and 'complete lifecycle management' but doesn't specify what analysis entails, what lifecycle stages are created, whether this is a write operation, permissions needed, or response format. For a 10-parameter mutation tool with no annotations, this leaves significant behavioral gaps.

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 lists the tool's core functions. It's front-loaded with the primary action ('add a new feature') and avoids redundancy. However, it could be more concise by removing 'or project' redundancy with 'PRD' and clarifying the relationship between analysis and task expansion.

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 complexity (10 parameters, 6 required, mutation tool), lack of annotations, and no output schema, the description is incomplete. It doesn't cover parameter details, behavioral expectations, error conditions, or return values. For a tool with significant scope ('analyze impact', 'expand into tasks', 'lifecycle management'), this leaves too much unspecified for reliable agent invocation.

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 0%, so the description must compensate for all 10 parameters. It mentions 'feature', 'PRD or project', 'impact analysis', 'actionable tasks', and 'lifecycle management', which loosely map to some parameters like 'featureIdea', 'targetPRD', 'targetProject', 'expandToTasks', and 'createLifecycle'. However, it doesn't explain the purpose or format of critical parameters like 'autoApprove', 'businessJustification', 'targetUsers', or 'requestedBy', leaving most parameters inadequately documented.

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 ('add', 'analyze', 'expand') and resources ('feature', 'PRD or project', 'actionable tasks', 'lifecycle management'). It distinguishes from siblings like 'enhance_prd' or 'create_issue' by focusing on feature addition with analysis and task expansion. However, it doesn't explicitly differentiate from 'expand_task' or 'generate_prd' in sibling context.

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. With many sibling tools for project/issue management (e.g., 'create_issue', 'enhance_prd', 'expand_task'), there's no indication of prerequisites, appropriate contexts, or exclusions. The agent must infer usage from the description alone.

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 but offers minimal behavioral insight. It states the action is 'Add' (implying a write/mutation operation) but doesn't disclose critical traits: whether it requires specific permissions, if it's idempotent (adding duplicate issues), what happens on failure (partial adds), rate limits, or response format. The description doesn't contradict annotations (none exist), but it's insufficient for a mutation 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 a single, direct sentence with zero wasted words—front-loaded and perfectly concise. It efficiently communicates the core action without unnecessary elaboration, making it easy 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 a mutation tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks details on behavior, error handling, parameter semantics, and integration with sibling tools (e.g., 'get_current_sprint'). For a tool that modifies data, this minimal description risks misuse or confusion by the 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 description coverage is 0%, so the description must compensate but adds no parameter details. It doesn't explain what 'sprintId' and 'issueIds' represent (e.g., numeric IDs, strings), their format, sourcing (from 'list_sprints' or 'list_issues'), or constraints (e.g., max issues per call). The description repeats the tool name without enhancing parameter understanding.

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') and target ('issues to an existing sprint'), making the purpose immediately understandable. It distinguishes from sibling tools like 'create_sprint' (creates new sprints) and 'remove_issues_from_sprint' (removes issues). However, it doesn't specify what type of 'issues' (e.g., tasks, bugs) or mention the resource context (e.g., project management 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., sprint must exist, issues must be unassigned), exclusions (e.g., cannot add issues to completed sprints), or related tools like 'create_issue' (to first create issues) or 'plan_sprint' (for broader sprint planning). Usage is implied but not explicitly defined.

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

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states the action is 'Add' (implying a write operation) but doesn't disclose permissions required, rate limits, whether the operation is idempotent, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is inadequate.

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, clear sentence with zero wasted words. It's front-loaded with the core action and target, making it easy to parse. Every word earns its place, though it could benefit from additional context.

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 3 required parameters, 0% schema coverage, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'adding an item' entails operationally, what the parameters mean, or what to expect upon completion. The agent lacks critical context for correct invocation.

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 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'projectId', 'contentId', or 'contentType' represent, their formats, or relationships. The enum for 'contentType' is documented in the schema but not explained in the description, leaving semantics unclear.

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') and target ('an item to a GitHub project'), making the purpose immediately understandable. It distinguishes from siblings like 'create_project' or 'update_project' by focusing on adding items to existing projects. However, it doesn't specify what types of items can be added beyond what's implied by the schema.

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 'add_issues_to_sprint' or 'create_issue'. It doesn't mention prerequisites (e.g., needing an existing project) or exclusions. The agent must infer usage from the tool name alone.

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 for behavioral disclosure. It mentions 'AI-powered analysis' which implies computational processing, but doesn't describe output format, potential latency, rate limits, authentication needs, or whether it's read-only vs. mutative. For an 8-parameter analysis tool with zero annotation coverage, this leaves significant behavioral gaps.

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 packs multiple analysis components. It's appropriately sized for the tool's complexity and front-loads the core purpose. No wasted words, though it could benefit from more structural separation of the different analysis aspects.

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 (8 parameters, 6 required), no annotations, no output schema, and 0% schema description coverage, the description is inadequate. It mentions analysis components but doesn't explain what the analysis produces, how parameters interact, or what the agent can expect as results. For an AI analysis tool with rich inputs, this leaves too many unanswered questions.

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 0%, so all 8 parameters are undocumented in the schema. The description mentions 'task complexity, effort estimation, risk assessment, and actionable recommendations' which partially maps to some parameters (e.g., taskTitle, taskDescription, includeRisks, includeRecommendations), but doesn't explain parameter purposes, formats, or relationships. It fails to compensate for the complete lack of schema 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 performs AI-powered analysis of task complexity with specific components: effort estimation, risk assessment, and actionable recommendations. It uses specific verbs ('analyze', 'estimate', 'assess', 'provide') and identifies the resource (task complexity). However, it doesn't explicitly differentiate from sibling tools like 'expand_task' or 'get_next_task' that might also involve task 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 no guidance on when to use this tool versus alternatives. With many sibling tools for task/project management (e.g., 'expand_task', 'plan_sprint', 'get_next_task'), there's no indication of appropriate contexts, prerequisites, or exclusions. The description only states what it does, not when it should be used.

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. It discloses that archiving hides items without deletion, which is useful behavioral context. However, it lacks details on permissions required, whether the action is reversible, rate limits, or what happens to dependent data, leaving significant gaps for a mutation 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 a single, efficient sentence that front-loads the core action and adds clarifying context without waste. Every word earns its place, making it easy 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?

For a mutation tool with no annotations, 0% schema coverage, and no output schema, the description is incomplete. It covers the basic effect of archiving but misses parameter details, usage context, permissions, and behavioral nuances needed for safe and effective use.

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 0%, and the description provides no information about the parameters 'projectId' and 'itemId'. It doesn't explain what these IDs represent, how to obtain them, or their format, failing to compensate for the lack of schema 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 action ('Archive an item') and resource ('in a GitHub project'), with specific clarification that archived items are hidden but not deleted. However, it doesn't explicitly differentiate from sibling tools like 'remove_project_item' or 'unarchive_project_item', which would require a 5.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'remove_project_item' or 'unarchive_project_item', nor does it mention prerequisites or context for archiving. It only explains what archiving does, not when it's appropriate.

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 for behavioral disclosure. While 'bulk assign' implies a write/mutation operation, it doesn't specify permissions required, whether the operation is reversible, what happens to previously assigned iterations, or any rate limits. This leaves significant behavioral gaps for a mutation 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 a single, efficient sentence that states the core functionality without unnecessary words. It's appropriately sized for what it communicates, though what it communicates is limited in scope.

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 4 parameters (0% schema coverage), no annotations, and no output schema, the description is inadequate. It doesn't explain what 'items' are (issues, tasks, etc.), what format IDs should use, what happens on success/failure, or provide any error handling context, leaving the agent poorly equipped to use this 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?

With 0% schema description coverage for all 4 parameters, the description provides no semantic information about what 'projectId', 'itemIds', 'iterationId', or 'fieldName' represent. The description mentions 'items' and 'iteration' which loosely map to two parameters, but doesn't explain their formats, relationships, or why 'fieldName' might be optional when others are required.

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 ('bulk assign') and target ('multiple items to a specific iteration'), providing a specific verb+resource combination. However, it doesn't differentiate from potential sibling tools like 'add_issues_to_sprint' or 'set_field_value' that might handle similar assignment operations, preventing a perfect score.

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. It doesn't mention prerequisites, constraints, or compare it to sibling tools like 'add_issues_to_sprint' or 'set_field_value' that might handle similar operations, leaving the agent with minimal context for proper 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, the description carries full burden. It states the tool 'removes/clears the value', implying a destructive mutation, but doesn't disclose permissions needed, whether the change is reversible, rate limits, or what happens to the field after clearing (e.g., becomes null/empty). It lacks behavioral context beyond the basic action.

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 wasted words. It's front-loaded with the core action and resource, making it easy 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?

For a destructive mutation tool with 3 undocumented parameters, 0% schema coverage, and no output schema, the description is inadequate. It doesn't compensate for the missing parameter documentation or provide necessary behavioral context, making it incomplete for safe and effective use.

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 0%, and the description provides no information about the three required parameters (projectId, itemId, fieldId). It doesn't explain what these IDs represent, their format, or how to obtain them, leaving parameters completely undocumented.

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 ('Clear a field value') and resource ('for a GitHub project item'), specifying it works for 'any field type'. It distinguishes from sibling 'set_field_value' by focusing on removal rather than assignment, though it doesn't explicitly name that sibling.

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 'set_field_value' (for setting values) or 'remove_project_item' (for deleting entire items). The description mentions it works for 'any field type' but doesn't specify prerequisites or constraints.

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 but only states the action ('create') without details on permissions, side effects, error handling, or response format. It misses critical information like whether this requires admin access, what happens on failure, or if it's idempotent.

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 no wasted words, making it easy to parse. It's appropriately sized for a basic purpose statement, though it lacks depth due to its brevity.

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 complexity (6 parameters with nested structures, no annotations, no output schema), the description is insufficient. It doesn't explain parameter meanings, behavioral traits, or expected outcomes, making it incomplete for effective tool use in this 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 0%, so the schema provides no parameter details. The description adds no information about the 6 parameters (e.g., what 'triggers' or 'actions' entail, format of 'projectId'), failing to compensate for the coverage gap and leaving parameters largely unexplained.

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 ('automation rule for a GitHub project'), making the purpose evident. However, it doesn't differentiate from sibling tools like 'update_automation_rule' or 'delete_automation_rule', which would require explicit scope or condition distinctions.

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 such as 'update_automation_rule' or 'list_automation_rules'. The description lacks context about prerequisites, typical scenarios, or exclusions, leaving usage ambiguous.

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 for behavioral disclosure. While it explains what draft issues are (native to Projects v2, no repository issue needed), it doesn't mention permissions required, whether this is a write operation (implied by 'Create' but not explicit), rate limits, or what happens on success/failure. For a creation tool with zero annotation coverage, this leaves significant behavioral gaps.

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 clear, efficient sentences with zero waste. The first sentence states the core purpose, the second provides important contextual differentiation. Every word earns its place and the information is front-loaded appropriately.

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 creation tool with 4 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. While it explains the 'what' and provides some GitHub-specific context about Projects v2, it lacks crucial information about parameters, permissions, behavioral outcomes, and what the tool returns upon success.

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% schema description coverage and 4 parameters (2 required), the description provides no information about any parameters. It doesn't explain what 'projectId' refers to, what format 'title' and 'body' should use, what 'assigneeIds' represent, or any constraints. The description fails to compensate for the complete lack of schema 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 ('Create a draft issue') and resource ('in a GitHub project'), with explicit differentiation from repository issues ('native to Projects v2 and don't require creating a repository issue first'). This distinguishes it from sibling tools like 'create_issue' which likely creates repository issues.

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 (for Projects v2 draft issues) by contrasting it with repository issues, which helps differentiate it from 'create_issue'. However, it doesn't explicitly mention when NOT to use it or provide alternatives beyond the implied 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Create a new GitHub issue' implies a write operation, but it doesn't disclose any behavioral traits such as required permissions, rate limits, whether it's idempotent, what happens on failure, or the format of the response. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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. It's front-loaded with the core action ('Create a new GitHub issue'), making it immediately clear. Every word earns its place, and there's no unnecessary elaboration 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 complexity (a mutation tool with 7 parameters, 4 required), lack of annotations, and no output schema, the description is incomplete. It doesn't provide enough context for safe and effective use—missing details on behavior, parameters, and expected outcomes. The agent would struggle to invoke this tool correctly without additional 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 0%, meaning none of the 7 parameters have descriptions in the schema. The tool description adds no information about parameters beyond what's implied by the tool name (e.g., 'title' and 'description' might be guessed). It doesn't explain what 'milestoneId', 'assignees', 'labels', 'priority', or 'type' mean or how to format them, failing to compensate for the low 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 'Create a new GitHub issue' clearly states the verb ('Create') and resource ('GitHub issue'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'add_issues_to_sprint' or 'update_issue', which also involve GitHub issue operations, so it doesn't reach the highest clarity level.

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. It doesn't mention prerequisites (e.g., needing repository access), when not to use it (e.g., for modifying existing issues), or point to sibling tools like 'update_issue' for different scenarios. The agent must infer usage from the tool name alone.

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 for behavioral disclosure. It states the action ('Add a comment') which implies a write operation, but doesn't cover permissions needed, rate limits, error conditions, or what happens on success (e.g., comment ID returned). This leaves significant gaps for a mutation 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 a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a simple tool and front-loads the essential 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?

For a mutation tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't address behavioral aspects like permissions or response format, leaving the agent with insufficient context to use the tool effectively beyond basic parameter inference.

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 0%, so the description must compensate for undocumented parameters. It mentions 'a GitHub issue' which hints at 'issueNumber', and 'comment' which relates to 'body', but doesn't explain parameter formats (e.g., issueNumber as integer, body as markdown) or constraints. This adds minimal value beyond the schema's structure.

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 GitHub issue'), making the purpose immediately understandable. It doesn't differentiate from siblings like 'update_issue_comment' or 'list_issue_comments', but it's specific enough to avoid confusion with unrelated 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 no guidance on when to use this tool versus alternatives like 'update_issue_comment' or 'delete_issue_comment', nor does it mention prerequisites such as issue existence or authentication requirements. It's a basic statement of function without contextual usage information.

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 'Create a new GitHub label' which implies a write operation, but doesn't disclose permissions needed, rate limits, whether it's idempotent, or what happens on duplicate names. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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 wasted words. It's appropriately sized for a simple creation tool and front-loads the essential information. Every word earns its place in conveying the core purpose.

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 this is a mutation tool with 3 parameters, 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, error conditions, or behavioral details needed for safe invocation. The description alone is insufficient for an agent to use this tool 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?

The schema description coverage is 0%, so the description must compensate but provides no parameter information. It doesn't explain what 'name', 'color', or 'description' parameters represent, their formats (e.g., color as hex code), or constraints. With 3 parameters and no schema descriptions, the description adds no semantic value beyond what's inferred from the tool name.

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 ('GitHub label'), making the purpose immediately understandable. It distinguishes from sibling tools like 'list_labels' by specifying creation rather than listing. However, it doesn't explicitly differentiate from other creation tools like 'create_issue' or 'create_milestone' beyond the resource type.

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. There's no mention of prerequisites (e.g., repository access), when not to use it (e.g., for existing labels), or comparisons to sibling tools like 'update_issue' for modifying labels. Usage is implied but not explicitly stated.

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 the full burden of behavioral disclosure. 'Create a new milestone' implies a write/mutation operation but provides no information about permissions required, whether the creation is reversible, what happens on success/failure, rate limits, or any side effects. For a mutation tool with zero annotation coverage, this is completely inadequate.

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 at just three words, which could be appropriate if it were more informative. However, this conciseness comes at the cost of under-specification rather than efficient communication. The structure is simple but lacks the necessary information density for a tool with multiple parameters and sibling alternatives.

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?

This is a mutation tool with 3 parameters, no annotations, no output schema, and multiple sibling alternatives, yet the description provides minimal information. It fails to explain what a milestone is in this context, how it differs from other project management artifacts, what the parameters mean, or what happens after creation. For a tool of this complexity and context, the description is completely inadequate.

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 description provides zero information about parameters. With 3 parameters (title, description, dueDate) and 0% schema description coverage, the schema only shows data types without explaining what these fields represent or their constraints. The description doesn't mention any parameters, their purposes, or provide any semantic context beyond what's in the bare 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 'Create a new milestone' is a tautology that merely restates the tool name without adding specificity. It doesn't distinguish this tool from sibling tools like 'create_project', 'create_sprint', or 'create_roadmap' that also create different resources. While the verb 'create' is clear, the description fails to provide meaningful differentiation from other creation tools in the server.

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 absolutely no guidance about when to use this tool versus alternatives. With multiple sibling creation tools (create_project, create_sprint, create_roadmap, create_issue, etc.), there's no indication of when a milestone is appropriate versus other project management artifacts. No prerequisites, context, or exclusions are mentioned.

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 the full burden of behavioral disclosure. It states 'Create a new GitHub project' which implies a write operation, but doesn't disclose any behavioral traits such as required permissions, rate limits, whether it's idempotent, what happens on duplicate titles, or what the response looks like. This is a significant gap for a mutation tool with zero annotation coverage.

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 wasted words. It's front-loaded with the core action and resource, making it immediately scannable and appropriately sized for its purpose.

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 this is a mutation tool with 4 parameters (3 required), 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain parameters, behavioral expectations, or return values, leaving significant gaps for an AI agent to understand 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?

The input schema has 4 parameters with 0% description coverage, meaning none have descriptions in the schema. The tool description provides no information about parameters, not even mentioning what they are or their purposes (title, shortDescription, owner, visibility). This fails to compensate for the schema's lack of 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 verb ('Create') and resource ('new GitHub project'), making the purpose immediately understandable. It distinguishes from siblings like 'update_project' or 'delete_project' by specifying creation. However, it doesn't explicitly differentiate from other creation tools like 'create_issue' or 'create_milestone' beyond the resource 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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication, repository context), when not to use it, or how it differs from similar creation tools like 'create_issue' or 'create_milestone' in the sibling list.

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 for behavioral disclosure. It states 'Create' which implies a write/mutation operation, but doesn't disclose permission requirements, rate limits, whether the operation is idempotent, what happens on failure, or the response format. This is a significant gap for a mutation tool with zero annotation coverage.

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, clear sentence with zero wasted words. It's appropriately sized for a basic tool definition and front-loads the essential information (create custom field). 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?

For a mutation tool with 6 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, error conditions, or behavioral aspects. The description should provide more context about the creation operation, parameter meanings, and expected outcomes.

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% schema description coverage for 6 parameters, the description provides no parameter information beyond what's inferred from the tool name. It doesn't explain what 'projectId', 'name', 'type', 'options', 'description', or 'required' mean, their formats, constraints, or relationships. The description fails to compensate for the complete lack of schema 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 action ('Create') and resource ('a custom field for a GitHub project'), making the purpose immediately understandable. It distinguishes from siblings like 'update_project_field' by specifying creation rather than modification, though it doesn't explicitly contrast with 'list_project_fields' or other field-related 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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing project), when not to use it (e.g., for updating existing fields), or direct alternatives like 'update_project_field' for modifications or 'list_project_fields' for viewing existing fields.

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 for behavioral disclosure. 'Create' implies a write/mutation operation, but the description doesn't disclose any behavioral traits: no information about permissions required, whether this is idempotent, what happens on duplicate names, rate limits, or what the response contains. For a mutation 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 gets straight to the point with no wasted words. It's appropriately sized for a tool with a clear primary function, though the brevity comes at the cost of completeness. Every word earns its place in conveying the core purpose.

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 3 required parameters, 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It states what the tool does at a high level but provides none of the contextual information needed for effective use: no parameter explanations, no behavioral context, no usage guidance, and no indication of what the tool returns. The agent would need to guess about many aspects of tool behavior.

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 0%, so the description must compensate by explaining parameters, but it provides no parameter information whatsoever. The three required parameters (projectId, name, layout) are completely undocumented in the description, and the layout enum values (board, table, timeline, roadmap) aren't mentioned or explained. The description adds zero value beyond what the bare schema 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 action ('Create a new view') and target resource ('for a GitHub project'), making the purpose immediately understandable. It distinguishes from siblings like 'create_project' or 'update_project_view' by specifying it's about views rather than projects themselves or updates to existing views. However, it doesn't specify what a 'view' entails in GitHub projects context.

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. It doesn't mention prerequisites (e.g., needing an existing project), differentiate from similar tools like 'create_project' or 'update_project_view', or indicate when this operation is appropriate versus other view-related operations. The agent must infer usage from the tool name alone.

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 for behavioral disclosure. It states this is a creation operation but doesn't mention authentication requirements, rate limits, whether it's idempotent, what happens on success/failure, or the response format. This is inadequate for a mutation tool with zero annotation coverage.

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 unnecessary words. It's appropriately sized for a basic tool description and front-loads the 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?

For a mutation tool with 5 parameters (3 required), 0% schema description coverage, no annotations, and no output schema, the description is severely incomplete. It doesn't explain parameter meanings, behavioral aspects, or what the tool returns, leaving significant gaps for an AI agent to use 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 0%, meaning none of the 5 parameters have schema descriptions. The tool description provides no information about what 'title', 'body', 'head', 'base', or 'draft' mean, their expected formats, or how they relate to GitHub pull request creation. This leaves critical parameter semantics undocumented.

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 ('create') and resource ('new pull request in a GitHub repository'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'update_pull_request' or explain how it differs from 'create_pull_request_review' in the same server.

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 'update_pull_request' or 'create_pull_request_review'. It also doesn't mention prerequisites (e.g., needing repository access) or when not to use it (e.g., for existing pull requests).

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 for behavioral disclosure. While 'create' implies a write operation, it doesn't disclose important behavioral traits: whether this requires specific permissions, if reviews are editable/deletable, rate limits, or what happens when multiple reviews are submitted. The description mentions the three event types but doesn't explain their consequences (e.g., APPROVE may merge the PR).

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 core purpose. Every word earns its place - 'create a review' establishes the action, 'on a pull request' specifies the context, and the parenthetical clarifies the three review types. No wasted words or redundant 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?

For a 4-parameter mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain the review creation process, expected responses, error conditions, or how parameters interact. While concise, it lacks the completeness needed for an agent to understand the full context of this write operation.

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 0%, so the description must compensate for undocumented parameters. It only mentions the 'event' parameter's three possible values but doesn't explain what 'pullNumber', 'body', or 'comments' represent or how they relate to the review. No guidance on parameter interactions (e.g., when 'comments' array should be used with specific events) is provided.

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 ('create a review') and resource ('on a pull request'), specifying the three possible review types (approve, request changes, or comment). It distinguishes from sibling tools like 'create_issue_comment' or 'update_pull_request' by focusing specifically on pull request reviews. However, it doesn't explicitly differentiate from 'list_pull_request_reviews' beyond the verb difference.

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. It doesn't mention prerequisites (e.g., needing access to the pull request), when to choose different event types, or how this differs from other comment/approval mechanisms. No explicit alternatives or exclusions 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?

With no annotations provided, the description carries full burden for behavioral disclosure. While 'create' implies a write operation, the description doesn't address permissions needed, whether this is an atomic operation, what happens on failure, or what the response contains. For a complex creation tool with nested objects, this leaves significant behavioral questions unanswered.

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 core purpose without unnecessary words. It's appropriately sized for a tool description and gets straight to the point with zero wasted text.

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

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

For a complex creation tool with nested parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what constitutes a successful creation, what gets returned, error conditions, or how this tool relates to the many sibling tools in the project management domain.

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% schema description coverage and 2 complex nested parameters, the description provides minimal parameter guidance. It mentions 'milestones and tasks' which hints at the 'milestones' parameter structure, but offers no insight into the 'project' parameter or the specific structure expected for either parameter. The description doesn't compensate for the schema's lack of 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 action ('create') and resource ('project roadmap with milestones and tasks'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from similar siblings like 'create_project' or 'create_milestone', which reduces its differentiation value.

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. With multiple sibling tools for creating projects, milestones, and other project management entities, there's no indication of when this comprehensive roadmap creation tool is preferred over more granular 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?

No annotations are provided, so the description carries the full burden. It states 'create' implies a write operation, but doesn't disclose behavioral traits like required permissions, whether it's idempotent, what happens on conflicts, or error handling. This leaves significant gaps for a mutation 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 a single, efficient sentence with no wasted words. It's front-loaded and appropriately sized for the tool's purpose, making it easy to scan and 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 complexity of a 5-parameter mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavior, parameter usage, and return values, making it insufficient for an AI agent to use the tool effectively without additional 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 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'issueIds' should contain, date formats for 'startDate' and 'endDate', or constraints on 'title' and 'description'. With 5 undocumented parameters, this is inadequate.

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 ('development sprint'), making the purpose evident. However, it doesn't differentiate from sibling tools like 'create_milestone' or 'create_project', which also create development artifacts, so it's not fully specific to sibling context.

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 'plan_sprint' or 'create_milestone'. The description lacks context about prerequisites, such as needing an existing project or milestone, or exclusions for overlapping functionality with siblings.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Create' implies a write/mutation operation, the description doesn't specify what gets created (file? database entry? visual artifact?), whether this operation is idempotent, what permissions are required, or what happens on failure. It mentions 'comprehensive' and 'full bidirectional traceability' but doesn't explain what those mean operationally.

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 communicates the core purpose. Every word earns its place - 'comprehensive' sets scope, the arrow notation clearly shows relationships, and 'full bidirectional traceability' specifies a key characteristic. There's no wasted verbiage 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?

For a complex tool with 8 parameters (all required), 0% schema description coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool produces (no output schema), doesn't clarify parameter purposes, and provides minimal behavioral context. While the purpose is clear, the description leaves too many operational questions unanswered for effective agent use.

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% schema description coverage for all 8 parameters, the description provides no information about what any parameter means or how they should be used. The description mentions 'PRD business requirements', 'features', 'use cases', and 'tasks' which loosely map to some parameters, but doesn't explain the purpose of 'includeUseCases', 'includeTraceabilityLinks', 'includeCoverageAnalysis', or 'validateCompleteness' parameters. The description fails to compensate for the complete lack of schema 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 ('Create a comprehensive requirements traceability matrix') and specifies the exact relationships being established ('linking PRD business requirements → features → use cases → tasks with full bidirectional traceability'). It distinguishes itself from sibling tools like 'create_project' or 'create_roadmap' by focusing specifically on traceability matrix creation rather than general project management artifacts.

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. It doesn't mention prerequisites (e.g., needing existing project data), doesn't specify when this tool is appropriate versus other creation tools like 'create_project' or 'create_roadmap', and offers no exclusions or limitations. The agent must infer usage context from the tool name and parameters alone.

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 the full burden of behavioral disclosure. While 'Delete' implies a destructive, irreversible mutation, the description does not specify permissions required, side effects (e.g., whether related data is affected), error conditions, or confirmation steps. This leaves significant gaps in understanding the tool's behavior and risks.

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 unnecessary words. It is front-loaded and wastes no space, making it easy 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 complexity of a destructive operation with no annotations, no output schema, and low parameter coverage, the description is incomplete. It lacks critical details about behavior, parameters, and outcomes, making it insufficient for safe and effective use by an AI 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 input schema has 0% description coverage, with one parameter ('ruleId') undocumented. The description does not add any meaning about this parameter, such as what a ruleId is, how to obtain it, or its format. Since schema coverage is low, the description fails to compensate, resulting in inadequate 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 action ('Delete') and the resource ('an automation rule from a project'), which is specific and unambiguous. However, it does not differentiate this tool from sibling tools like 'disable_automation_rule' or 'delete_project', leaving room for potential confusion about when to use this versus those alternatives.

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 such as 'disable_automation_rule' (which might temporarily deactivate instead of permanently delete) or 'delete_project' (which removes an entire project). It lacks explicit context, prerequisites, or exclusions, offering minimal usage direction.

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 destructive action ('Delete') but doesn't mention critical details like whether this operation is reversible, what permissions are required, potential side effects, or error conditions. This leaves significant gaps for safe tool invocation.

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, clear sentence with zero wasted words. It's appropriately sized for a simple operation and front-loads the essential information (action + resource). 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?

For a destructive operation with no annotations, no output schema, and undocumented parameters, the description is incomplete. It doesn't address behavioral risks, parameter meaning, or expected outcomes. Given the complexity of a delete operation in a GitHub context, more contextual information is needed.

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% description coverage, and the tool description provides no information about the 'draftIssueId' parameter. It doesn't explain what a draft issue ID is, where to find it, or its expected format. The description fails to compensate for the complete lack of schema 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 action ('Delete') and resource ('a draft issue from a GitHub project'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from sibling tools like 'delete_issue' or 'delete_project_item', which reduces clarity about its unique 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 provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (e.g., needing a draft issue ID), exclusions (e.g., cannot delete published issues), or comparisons to sibling tools like 'delete_issue' or 'remove_project_item'.

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 action ('Delete') which implies a destructive mutation, but doesn't mention critical details like whether deletion is permanent, what permissions are required, or what happens on success/failure. This leaves significant gaps for a mutation 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 a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a simple tool and front-loads the 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?

For a destructive mutation tool with no annotations, 0% schema description coverage, and no output schema, the description is inadequate. It doesn't address behavioral implications, error conditions, or what the tool returns, leaving the agent with insufficient context to use it safely and 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?

The schema description coverage is 0%, meaning the single parameter 'commentId' is undocumented in the schema. The description doesn't add any parameter-specific information beyond what's implied by the tool name, so it doesn't compensate for the schema gap. However, with only one parameter, the baseline is higher than for multi-parameter tools.

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 ('Delete') and target resource ('a comment from a GitHub issue'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'delete_draft_issue' or 'delete_milestone' beyond the resource type, which prevents a perfect score.

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, prerequisites (e.g., permissions needed), or exclusions. It lacks context about sibling tools like 'update_issue_comment' or 'list_issue_comments' that might be relevant 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. 'Delete' implies a destructive, irreversible mutation, but the description doesn't specify permissions required, confirmation prompts, error handling, or what happens to associated issues. For a destructive tool with zero annotation coverage, this leaves critical behavioral traits unaddressed.

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 wasted words. It's front-loaded with the core action and resource, making it immediately scannable. Every word earns its place, and there's no redundancy 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?

For a destructive mutation tool with no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks critical context: behavioral risks, parameter details, error cases, and output expectations. The description doesn't compensate for the missing structured data, leaving the agent under-informed about a high-stakes operation.

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 0%, so the description must compensate for undocumented parameters. It mentions 'milestone' but doesn't explain what 'milestoneId' represents (e.g., numeric ID vs. title), how to obtain it, or format constraints. With 1 parameter fully undocumented in the schema, the description adds minimal semantic value beyond the tool name.

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 ('Delete') and resource ('a GitHub milestone'), making the purpose immediately understandable. It distinguishes from siblings like 'update_milestone' and 'list_milestones' by specifying the destructive action. However, it doesn't explicitly differentiate from other deletion tools like 'delete_project', so it's not fully sibling-aware.

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. It doesn't mention prerequisites (e.g., needing milestone ID from 'list_milestones' or 'get_milestone_metrics'), exclusions, or comparisons to similar tools like 'update_milestone' for modification instead of deletion. Usage is implied but not explicitly stated.

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 the full burden of behavioral disclosure. 'Delete' implies a destructive, irreversible mutation, but the description doesn't specify permissions required, whether deletion is permanent or recoverable, rate limits, or what happens to associated items (e.g., issues, milestones). For a destructive tool with zero annotation coverage, this is a significant gap in transparency.

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 wasted words. It's front-loaded with the core action and resource, making it immediately scannable. Every word ('Delete', 'a', 'GitHub', 'project') earns its place by specifying the operation and target.

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 destructive nature, lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't address critical context like permissions, irreversibility, error conditions, or effects on related data. For a deletion tool in a project management context, this leaves too many unknowns for safe and effective use.

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 0%, so the description must compensate for undocumented parameters. The description mentions 'a GitHub project', which hints that 'projectId' refers to a GitHub project identifier, but doesn't clarify format (e.g., numeric ID, URL slug) or where to find it. It adds minimal meaning beyond the schema's property name, partially compensating for the coverage gap.

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 ('Delete') and resource ('a GitHub project'), making the purpose immediately understandable. It distinguishes itself from sibling tools like 'update_project' or 'get_project' by specifying a destructive operation. However, it doesn't explicitly differentiate from 'delete_milestone' or other deletion tools in terms of scope or hierarchy.

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. It doesn't mention prerequisites (e.g., needing admin permissions), consequences (e.g., irreversible deletion), or when to choose 'delete_project' over other tools like 'remove_project_item' or 'update_project' with status changes. Usage is implied only by the tool name.

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 but lacks behavioral details. It states the action ('Delete') which implies a destructive operation, but doesn't specify if deletion is permanent, requires specific permissions, has confirmation steps, or what happens on success/failure. This leaves critical behavioral aspects unclear for a mutation 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 a single, clear sentence with no wasted words. It's appropriately sized for a simple operation and front-loads the essential action and resource, making it easy 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?

For a destructive mutation tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is insufficient. It lacks behavioral context, parameter details, usage guidance, and expected outcomes, leaving too many gaps for effective tool invocation.

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 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'projectId' and 'viewId' represent, their format (e.g., numeric IDs, names), or how to obtain them (e.g., from 'list_project_views'). This leaves both parameters semantically undefined.

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 ('Delete') and resource ('a view from a GitHub project'), making the purpose immediately understandable. It doesn't distinguish from sibling tools like 'delete_project' or 'delete_automation_rule', but the specificity of 'view' provides adequate differentiation for a basic understanding.

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. It doesn't mention prerequisites (e.g., needing an existing view), exclusions, or related tools like 'list_project_views' for selection or 'update_project_view' for modification instead of deletion.

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, the description carries full burden. It states the tool disables a rule without deletion, which implies a state change (mutation) but doesn't disclose permissions needed, whether the change is reversible, side effects, or error conditions. Minimal behavioral context is provided.

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 core action and adds a clarifying constraint ('without deleting it'). Zero waste, appropriately sized for the tool's simplicity.

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 no annotations and no output schema, the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage context, or output. Given the tool's straightforward nature, it's passable but leaves gaps in guidance and transparency.

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 0%, but the single parameter 'ruleId' is self-explanatory. The description doesn't add any parameter details beyond what the schema name implies. Baseline is 3 since the parameter is simple and the schema covers it structurally, though no semantic enrichment 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 action ('Disable') and the resource ('an automation rule'), and specifies the effect ('without deleting it'). It distinguishes from 'delete_automation_rule' by noting preservation, but doesn't explicitly differentiate from 'enable_automation_rule' or other rule-related 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?

No guidance is provided on when to use this tool versus alternatives like 'enable_automation_rule', 'update_automation_rule', or 'delete_automation_rule'. The description implies it's for temporarily deactivating rules, but lacks explicit context or prerequisites.

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 the full burden of behavioral disclosure. It states the action ('Enable') but doesn't describe what enabling entails (e.g., whether it activates the rule immediately, requires permissions, has side effects like notifications, or what happens if the rule is already enabled). For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding the tool's behavior and implications.

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 wasted words. It's front-loaded with the core purpose and appropriately sized for a simple tool. Every word earns its place by directly conveying the tool's function 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 (a mutation operation), lack of annotations, no output schema, and minimal parameter guidance, the description is incomplete. It doesn't address behavioral aspects like permissions, side effects, error conditions, or return values. For a tool that modifies system state, this leaves the agent with insufficient context to use it safely and 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?

The input schema has one parameter ('ruleId') with 0% description coverage, meaning the schema provides no semantic context. The description doesn't add any parameter-specific information beyond implying that 'ruleId' identifies the rule to enable. It doesn't explain the format of 'ruleId' (e.g., numeric ID, UUID, name) or where to find it. With low schema coverage, the description compensates minimally, resulting in a baseline score.

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 ('Enable') and the target resource ('a disabled automation rule'), providing a specific verb+resource combination. It distinguishes from sibling tools like 'disable_automation_rule' and 'create_automation_rule' by focusing on enabling existing disabled rules rather than creating new ones or disabling them. However, it doesn't explicitly differentiate from 'update_automation_rule' which might also modify rule states.

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. It doesn't mention prerequisites (e.g., the rule must exist and be disabled), when not to use it, or direct alternatives like 'update_automation_rule' for state changes. The agent must infer usage from the tool name and description alone without explicit context.

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 mentions 'AI-powered improvements' but doesn't detail how the enhancement works, what permissions or inputs are needed, whether changes are reversible, or what the output format looks like. For a tool with 9 parameters and no annotations, this leaves significant gaps in understanding its behavior.

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 core purpose. It avoids redundancy and wastes no words, though it could benefit from more structured guidance for such a complex tool.

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

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

Given the complexity (9 parameters, 7 required), lack of annotations, and no output schema, the description is incomplete. It doesn't explain how enhancements are applied, what the output entails, or handle the many parameters. For a tool with this level of complexity, more detail is needed to guide effective use.

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 0%, so the description must compensate for undocumented parameters. It mentions 'adding missing elements, improving clarity, and providing comprehensive analysis,' which loosely relates to some parameters like 'addMissingElements' and 'improveExisting,' but doesn't explain the purpose or usage of the 9 parameters (e.g., 'enhancementType', 'focusAreas', 'includeResearch'). This fails to adequately clarify 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 tool's purpose: 'Enhance an existing PRD with AI-powered improvements, adding missing elements, improving clarity, and providing comprehensive analysis.' It specifies the verb ('enhance'), resource ('existing PRD'), and scope ('AI-powered improvements'), though it doesn't explicitly distinguish it from sibling tools like 'generate_prd' or 'parse_prd'.

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. It doesn't mention sibling tools like 'generate_prd' (for creating new PRDs) or 'parse_prd' (for analyzing existing ones), nor does it specify prerequisites or exclusions for 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 full burden for behavioral disclosure. It states the tool 'automatically adds' metadata, implying a write/mutation operation, but doesn't clarify permissions required, whether changes are reversible, rate limits, or what happens if enrichment fails. For a tool with 7 parameters and no annotation coverage, this leaves significant behavioral gaps.

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—two brief sentences that efficiently state the core functionality. Every word earns its place with no redundancy or fluff. It's front-loaded with the main purpose ('AI-powered issue enrichment') followed by specific actions.

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 complexity (7 parameters, no annotations, no output schema), the description is incomplete. It explains what the tool does at a high level but lacks crucial details: parameter meanings, behavioral traits (e.g., mutation impact), output format, or error handling. For a tool that likely modifies issues, this leaves too many unknowns for reliable agent use.

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 0%, so the schema provides no parameter documentation. The description doesn't mention any parameters, leaving all 7 (including 4 required) unexplained. It fails to compensate for the coverage gap, not even hinting at what 'projectId', 'issueId', etc., represent or how 'autoApply' affects 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 tool's purpose: 'AI-powered issue enrichment' with specific actions like 'adds labels, priority, type, complexity, and effort estimates.' It distinguishes from siblings like 'triage_issue' or 'analyze_task_complexity' by focusing on automated enrichment rather than manual triage or standalone analysis. However, it doesn't explicitly differentiate from 'enrich_issues_bulk' regarding batch vs. single issue processing.

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. It doesn't mention prerequisites (e.g., needing an existing issue), compare to 'enrich_issues_bulk' for multiple issues, or specify when automated enrichment is appropriate versus manual updates. The description implies usage for AI-driven metadata addition but lacks explicit context 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'AI-powered' which hints at automation, but doesn't explain what 'enrichment' entails (e.g., adding metadata, categorizing, prioritizing), whether it's read-only or mutative, what permissions are needed, or any rate limits. For a bulk operation with no annotation coverage, this leaves significant behavioral gaps.

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's front-loaded with key information ('Bulk AI-powered issue enrichment'). There's no wasted verbiage, though it could benefit from slightly more detail given the complexity. It's appropriately sized for a tool description.

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 complexity (bulk AI operation with 4 parameters), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't explain what enrichment does, what the inputs mean, what the output looks like, or any behavioral constraints. For a tool that likely performs significant processing, this leaves too much undefined.

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 0%, so the description must compensate for all 4 undocumented parameters. The description mentions 'bulk' and 'multiple issues' which loosely relates to 'issueIds', but doesn't explain 'projectId', 'projectContext', or 'autoApply' parameters. It adds minimal semantic value beyond what's implied by the tool name, failing to address the coverage gap.

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 'Bulk AI-powered issue enrichment for multiple issues at once' clearly states the action (enrichment) and resource (issues), and specifies it's for multiple issues. However, it doesn't distinguish this from the sibling 'enrich_issue' tool, which appears to be a single-issue version. The purpose is clear but lacks sibling differentiation.

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 'enrich_issue' or other issue-related tools. It mentions 'bulk' and 'multiple issues at once' which implies scale, but doesn't specify thresholds, prerequisites, or when not to use it. No explicit alternatives or contextual boundaries 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'AI-powered analysis' and 'implementation recommendations', but doesn't clarify output format, whether it's read-only or mutating, potential rate limits, or error conditions. For a tool with 11 parameters and no annotations, this leaves significant gaps in understanding how the tool behaves.

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 core functionality. It's front-loaded with the main purpose and includes key features without unnecessary elaboration. However, given the complexity of the tool (11 parameters, no annotations), it might be too brief to be fully helpful.

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

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

For a complex tool with 11 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It explains what the tool does at a high level but provides no guidance on usage, parameter meanings, behavioral traits, or expected outputs. The agent would struggle to use this tool effectively without additional 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 0%, meaning none of the 11 parameters have descriptions in the schema. The tool description doesn't mention any parameters at all, failing to compensate for this gap. Parameters like 'currentComplexity', 'maxSubtasks', 'targetComplexity', and 'projectType' remain completely unexplained in both schema and 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's purpose: 'Break down a complex task into smaller, manageable subtasks with AI-powered analysis, dependency detection, and implementation recommendations.' It specifies the verb ('break down'), resource ('complex task'), and key features (AI analysis, dependency detection, recommendations). However, it doesn't explicitly distinguish this from sibling tools like 'analyze_task_complexity' or 'plan_sprint', which might have overlapping functionality in task 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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, ideal scenarios, or exclusions. Given the many sibling tools for task/project management (e.g., 'analyze_task_complexity', 'plan_sprint', 'create_issue'), the agent lacks context on when this specific breakdown tool is appropriate.

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 the full burden of behavioral disclosure. It mentions 'AI analysis and industry best practices,' which hints at generative behavior, but doesn't clarify critical aspects like whether this is a read-only or mutating operation, what permissions are needed, how long it takes, or what the output format is. For a tool with 9 parameters and no annotations, this leaves significant gaps in understanding its behavior.

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 core purpose without unnecessary words. It's front-loaded with the main action ('Generate a comprehensive Product Requirements Document') and includes key details ('from a project idea using AI analysis and industry best practices') in a logical flow. Every part of the 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 complexity (9 parameters, 5 required), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a PRD document, a structured output), how errors are handled, or the scope of 'AI analysis.' For a generative tool with multiple inputs, more context is needed to ensure proper 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 description coverage is 0%, meaning none of the 9 parameters have descriptions in the schema. The tool description doesn't mention any parameters, so it adds no semantic information beyond what's inferred from parameter names (e.g., 'projectIdea' likely contains the idea text). This fails to compensate for the lack of schema documentation, making parameter usage unclear.

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: 'Generate a comprehensive Product Requirements Document (PRD) from a project idea using AI analysis and industry best practices.' It specifies the verb ('Generate'), resource ('Product Requirements Document'), and method ('using AI analysis and industry best practices'). However, it doesn't explicitly differentiate from sibling tools like 'enhance_prd' or 'parse_prd', which prevents a perfect score.

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. It doesn't mention sibling tools like 'enhance_prd' (which might modify existing PRDs) or 'parse_prd' (which might analyze PRDs), nor does it specify prerequisites or exclusions. The only implied usage is for creating PRDs from project ideas, but this is too vague for effective 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'AI-powered' and 'automatically', hinting at automation, but lacks critical details: whether this is a read-only or mutating operation (likely mutating given 'creates'), permission requirements, rate limits, side effects (e.g., if it modifies existing data), or output format. For a tool with 6 parameters and no annotations, 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 extremely concise—two short sentences with zero wasted words. It's front-loaded with the core purpose ('AI-powered roadmap generation from project issues') and adds a clarifying detail. Every sentence earns its place by conveying essential information efficiently.

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

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

Given the tool's complexity (6 parameters, no annotations, no output schema), the description is incomplete. It lacks behavioral context (e.g., mutation effects), parameter explanations, and output details. While conciseness is high, the description doesn't provide enough information for an agent to confidently invoke this tool without guessing at semantics or side effects.

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 0%, so the schema provides no parameter details. The description adds minimal value: it implies parameters relate to 'project issues' and outputs like 'milestones, sprints, and phases', but doesn't explain any of the 6 parameters (e.g., what 'autoCreate' does or how 'sprintDurationWeeks' is used). With low coverage, the description fails to compensate, leaving parameters largely undocumented.

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: 'AI-powered roadmap generation from project issues. Creates milestones, sprints, and phases automatically.' It specifies the verb ('generation'), resource ('roadmap'), and source ('project issues'), and distinguishes it from siblings like 'create_roadmap' by emphasizing AI automation. However, it doesn't explicitly contrast with all similar tools like 'plan_sprint' or 'create_milestone', keeping it at 4 rather than 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., existing project issues), exclusions, or compare it to siblings like 'create_roadmap' or 'plan_sprint'. The agent must infer usage from the purpose alone, which is insufficient for optimal 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 full burden but only states what the tool does without behavioral details. It doesn't disclose whether this is a read-only operation, what permissions are required, error handling, or response format, leaving significant gaps for a tool that likely accesses sensitive automation data.

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 gets straight to the point with no wasted words. It's appropriately sized for a simple retrieval tool and front-loads the 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?

For a tool with no annotations, 0% schema coverage, and no output schema, the description is insufficient. It doesn't explain what 'details' include, how to handle errors, or provide context about automation rules in this system, making it inadequate for safe and effective use by an AI 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 description coverage is 0% with one required parameter 'ruleId'. The description mentions 'a specific automation rule' which implies the parameter identifies the rule, but doesn't explain what a ruleId is, its format, or where to obtain it, failing to compensate for the schema's lack of 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 verb ('Get details') and resource ('of a specific automation rule'), making the purpose understandable. It distinguishes from sibling 'list_automation_rules' by specifying retrieval of a single rule rather than listing multiple, though this distinction could be more explicit.

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 'list_automation_rules' or 'get_issue' for related data. The description implies usage when details of a specific rule are needed, but lacks explicit context or prerequisites.

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 mentions the tool gets data based on today's date, but doesn't disclose behavioral traits such as whether it's read-only, what permissions are needed, how it handles missing data, or the return format. This leaves significant gaps for a tool with parameters.

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 core purpose without unnecessary words. Every part of the sentence earns its place by specifying the action, resource, and key constraint (today's date).

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 complexity (2 parameters, no annotations, no output schema), the description is incomplete. It lacks details on parameter usage, behavioral context, and output expectations, making it insufficient for an agent to reliably invoke the tool without additional assumptions.

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 0%, so the description must compensate for undocumented parameters. It adds no meaning about the two parameters (projectId and fieldName), failing to explain what they represent, their expected values, or how they affect the output. This is inadequate given the coverage gap.

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 ('Get') and the resource ('currently active iteration'), specifying it's based on today's date. It distinguishes from siblings like 'get_iteration_by_date' by focusing on the current iteration rather than a specific date, but doesn't explicitly mention this 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?

No guidance on when to use this tool versus alternatives like 'get_iteration_by_date' or 'get_current_sprint' is provided. The description implies usage for retrieving the current iteration but offers no context about prerequisites, alternatives, 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a 'Get' operation, implying it's likely read-only, but doesn't confirm this or describe any other behavioral traits like authentication needs, rate limits, error conditions, or what 'active' means in this context. The description is too minimal for a tool with parameters.

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, clear sentence with no wasted words. It's front-loaded with the core purpose and appropriately sized for what it conveys, though it lacks necessary detail for full tool understanding.

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 has one undocumented parameter, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'includeIssues' does, what the return value looks like, or any behavioral context needed for proper tool invocation in a system with many sibling sprint-related tools.

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 one required parameter 'includeIssues' with 0% description coverage in the schema, and the tool description provides no information about parameters. The description doesn't mention 'includeIssues' at all, leaving the parameter completely undocumented and unexplained.

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 'Get the currently active sprint' clearly states the verb ('Get') and resource ('currently active sprint'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_sprint_metrics' or 'list_sprints' that might also retrieve sprint information, preventing a perfect score.

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 'get_sprint_metrics' or 'list_sprints'. It doesn't mention prerequisites, exclusions, or specific contexts where this tool is preferred, leaving the agent to infer usage from the name alone.

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 for behavioral disclosure. While it indicates this is a read operation ('Get') and specifies supported field types, it doesn't disclose important behavioral aspects like authentication requirements, rate limits, error handling, response format, or whether it requires specific permissions. The description provides basic functional context but misses critical operational details.

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 efficiently structured in a single sentence that front-loads the core purpose. While concise, it could potentially benefit from slightly more detail given the complete lack of parameter documentation and behavioral context in annotations.

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 3 required parameters, 0% schema description coverage, no annotations, and no output schema, the description is insufficiently complete. It explains what the tool does at a high level but fails to provide necessary context about parameter meanings, expected inputs, return values, or operational constraints that would enable effective use.

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% schema description coverage for 3 required parameters, the description provides no information about what projectId, itemId, or fieldId represent, their expected formats, or how to obtain them. The description mentions 'GitHub project item' and 'field types' but doesn't connect these concepts to the specific parameters, leaving significant semantic gaps.

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 a field value') and resource ('for a GitHub project item'), and distinguishes it from siblings by specifying it supports reading all field types. This provides precise differentiation from tools like 'set_field_value' (write operation) and 'list_project_fields' (list operation).

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 reads field values for project items, but doesn't explicitly state when to use this tool versus alternatives like 'get_project' or 'get_issue'. No guidance is provided about prerequisites, error conditions, or when not to use this 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves details but does not specify whether this is a read-only operation, what permissions are required, how errors are handled, or the format of returned data. For a tool with zero annotation coverage, this leaves critical behavioral traits undocumented.

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, direct sentence with no wasted words, making it easy to parse. It front-loads the core purpose ('Get details') efficiently, though this brevity contributes to gaps in other dimensions. Every word serves a clear function.

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 lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It does not address behavioral traits, parameter details, or return values, leaving the agent with insufficient information to use the tool effectively in a complex environment with many sibling tools.

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 0%, and the description does not explain the 'issueId' parameter beyond implying it identifies a specific issue. It lacks details on format (e.g., numeric ID, URL), validation, or examples. With one required parameter and no schema descriptions, the description fails to compensate for the coverage gap.

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 ('Get') and resource ('details of a specific GitHub issue'), making the purpose immediately understandable. It distinguishes from siblings like 'list_issues' by specifying retrieval of a single issue rather than listing multiple. However, it lacks specificity about what details are included, which prevents a perfect score.

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 'list_issues' or 'update_issue'. It does not mention prerequisites (e.g., needing an issue ID) or contextual factors, leaving the agent to infer usage from the name alone. This minimal guidance is insufficient for effective 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool finds iterations by date but doesn't explain what 'iteration' means in this context, whether it's read-only or has side effects, error handling, or return format. This is a significant gap for a tool with no annotation coverage.

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 unnecessary words. It's appropriately sized and front-loaded, making it easy 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 complexity (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what an 'iteration' is, how parameters interact, or what the output looks like, leaving the agent with insufficient context to use the tool 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 0%, meaning none of the three parameters (projectId, date, fieldName) are documented in the schema. The description mentions 'date' but doesn't explain its format or purpose, and it omits 'projectId' and 'fieldName' entirely. It adds minimal value beyond the schema, failing to compensate for the coverage gap.

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 'Find which iteration contains a specific date' clearly states the tool's purpose with a specific verb ('Find') and resource ('iteration'), and it distinguishes the tool by focusing on date-based iteration lookup. However, it doesn't explicitly differentiate from sibling tools like 'get_current_iteration' or 'get_iteration_items', which might have overlapping functionality.

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. It doesn't mention prerequisites, context (e.g., project management scenarios), or comparisons to sibling tools like 'get_current_iteration' or 'get_iteration_configuration', leaving the agent to infer usage from the tool name alone.

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 for behavioral disclosure. It states this is a 'Get' operation (implying read-only), but doesn't mention permissions, rate limits, error conditions, or what happens if the project/field doesn't exist. For a tool with 2 parameters and no annotation coverage, this leaves significant behavioral gaps.

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 core purpose. Every word contributes to understanding what the tool does. There's no redundancy 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?

For a tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain parameter meanings, return format, error handling, or behavioral constraints. While concise, it doesn't provide enough context for reliable tool invocation given the lack of structured documentation.

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 0%, so the schema provides no parameter documentation. The description mentions 'iteration field configuration' but doesn't explain what 'projectId' or 'fieldName' parameters represent, their formats, or examples. While it hints at what data is returned, it adds minimal value beyond the parameter names themselves.

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 ('Get') and the resource ('iteration field configuration') with specific attributes ('duration, start date, and list of all iterations'). It distinguishes from siblings like 'get_current_iteration' or 'get_iteration_by_date' by focusing on configuration rather than current/date-specific data. However, it doesn't explicitly contrast with these siblings in the text.

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 'get_current_iteration' or 'get_iteration_by_date'. The description implies it retrieves configuration data, but doesn't specify use cases, prerequisites, or exclusions. The agent must infer usage from the tool name and description alone.

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 a read operation ('Get') but lacks details on permissions, rate limits, pagination (despite a 'limit' parameter), return format, or error handling. For a tool with 3 parameters and no annotation coverage, this is insufficient behavioral 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 a single, front-loaded sentence with zero waste. It efficiently conveys the core purpose without unnecessary elaboration, making it appropriately concise for a basic retrieval 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 3 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks details on parameter semantics, behavioral traits, and return values, leaving significant gaps for an AI agent to understand 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 description coverage is 0%, so the description must compensate. It mentions 'specific iteration' which hints at 'iterationId', but doesn't explain 'projectId', 'limit', or their relationships. The description adds minimal value beyond the bare schema, failing to clarify parameter meanings or usage.

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 ('Get') and resource ('all items assigned to a specific iteration'), making the purpose understandable. It doesn't explicitly distinguish from siblings like 'get_current_iteration' or 'list_project_items', but the focus on iteration-specific items is reasonably specific.

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 'list_project_items' or 'get_current_iteration'. The description implies usage for iteration-specific retrieval but doesn't specify prerequisites, exclusions, or comparative contexts 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 the full burden of behavioral disclosure. It states it 'gets' metrics (implying read-only), but doesn't specify what 'progress metrics' includes (e.g., completion percentage, issue counts, timelines), whether it requires specific permissions, or how it handles errors. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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, focused sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple retrieval tool. Every word earns its place by specifying what's being retrieved and for what resource.

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 2 required parameters, 0% schema description coverage, no annotations, and no output schema, the description is insufficient. It doesn't explain what metrics are returned, what 'includeIssues' controls, or provide any behavioral context. The agent would struggle to use this effectively without additional documentation or trial-and-error.

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 0%, so the schema provides no parameter documentation. The description mentions 'specific milestone' which hints at 'milestoneId', but doesn't explain what 'includeIssues' does or why it's required. It adds minimal semantic value beyond what can be inferred from parameter names, failing to compensate for the complete lack of 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 verb 'Get' and the resource 'progress metrics for a specific milestone', making the purpose unambiguous. It distinguishes from siblings like 'get_milestones' (list) and 'get_sprint_metrics' (different resource), though it doesn't explicitly name these alternatives. The specificity of 'progress metrics' provides good differentiation.

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 'get_milestones' (which might list milestones without metrics) or 'get_sprint_metrics' (for sprint-level metrics). There's no mention of prerequisites, context, or exclusion criteria. The agent must infer usage from the name and description alone.

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 the full burden. It mentions 'AI-powered recommendations' which hints at algorithmic behavior, but doesn't disclose critical traits like whether this is a read-only operation, what permissions are needed, how recommendations are generated, rate limits, or what the output format looks like. For a tool with 10 parameters and no annotations, 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, well-structured sentence that efficiently conveys the core purpose. It's front-loaded with the main action and includes key criteria without unnecessary elaboration. However, it could be slightly more concise by removing redundant phrasing like 'to work on'.

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 complexity (10 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the tool's behavior, parameter usage, or output format. For a recommendation tool with many inputs and no structured documentation, this leaves too many gaps for an AI 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 0%, so the schema provides no parameter documentation. The description mentions general criteria like 'priorities, dependencies, team capacity, and current project state' which loosely map to some parameters (e.g., 'teamSkills', 'sprintCapacity', 'currentPhase'), but it doesn't explain what any of the 10 parameters actually mean, their formats, or how they influence recommendations. This fails to compensate for the lack of 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: 'Get AI-powered recommendations for the next task to work on' with specific criteria like priorities, dependencies, team capacity, and project state. It uses a specific verb ('Get') and resource ('recommendations'), but doesn't explicitly distinguish it from siblings like 'analyze_task_complexity' or 'plan_sprint' which might have overlapping functionality.

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. It doesn't mention prerequisites, exclusions, or compare it to sibling tools like 'get_current_sprint' or 'list_issues' that might provide related information. Usage is implied by the purpose but not explicitly stated.

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 it 'gets a list', implying a read-only operation, but doesn't cover permissions, rate limits, pagination, or response format. For a tool with two required parameters and no output schema, this leaves critical behavioral traits undocumented.

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—it directly states the purpose without fluff. It's appropriately sized for a simple tool, though this conciseness comes at the cost of detail in other dimensions.

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 (2 required parameters, no annotations, no output schema), the description is incomplete. It lacks parameter explanations, behavioral context, and output details, making it inadequate for reliable agent use without additional inference or trial-and-error.

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 0%, so the description must compensate for undocumented parameters. It mentions no parameters at all, failing to explain 'limit' (e.g., max results) or 'includeIssues' (e.g., whether to fetch related issues). This leaves the agent guessing about input semantics beyond basic types.

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 'Get a list of overdue milestones' clearly states the verb ('Get') and resource ('overdue milestones'), but it's vague about scope and doesn't distinguish from siblings like 'list_milestones' or 'get_upcoming_milestones'. It specifies the 'overdue' filter but doesn't clarify what constitutes 'overdue' or how results are determined.

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 'list_milestones' or 'get_upcoming_milestones'. The description implies it's for retrieving overdue items, but it doesn't specify prerequisites, context, or exclusions, leaving the agent to guess based on tool names alone.

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 the full burden of behavioral disclosure. It states it 'gets details' but doesn't clarify if this is a read-only operation, what permissions are required, whether it's idempotent, or how errors are handled (e.g., invalid project IDs). For a tool with zero annotation coverage, this leaves critical behavioral traits unspecified.

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 with zero wasted words—it directly states the tool's function. Every word ('Get details of a specific GitHub project') earns its place by clarifying scope and resource, 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 low complexity (1 parameter, no nested objects) but lack of annotations and output schema, the description is incomplete. It doesn't explain what 'details' include in the return value, error conditions, or dependencies on other tools (e.g., 'projectId' from 'list_projects'). For a read operation with no structured output documentation, more context is needed to guide effective use.

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 1 parameter ('projectId') with 0% description coverage, so the schema provides no semantic context. The description implies the parameter identifies 'a specific GitHub project' but doesn't specify format (e.g., numeric ID, URL) or sourcing (e.g., from 'list_projects'). This adds minimal meaning beyond the schema's structural definition, aligning with the baseline for low 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 verb ('Get') and resource ('details of a specific GitHub project'), making the purpose unambiguous. It distinguishes from siblings like 'list_projects' (which returns multiple projects) by specifying retrieval of a single project's details. However, it doesn't specify what 'details' include (e.g., fields, metadata), which prevents a perfect score.

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. It doesn't mention prerequisites (e.g., needing a project ID), contrast with 'list_projects' for browsing, or specify scenarios like retrieving metadata for editing. Without such context, the agent must infer usage from the name alone.

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 the full burden of behavioral disclosure. It states the tool retrieves README content, implying a read-only operation, but does not specify if it requires authentication, rate limits, error handling, or the format of the returned content. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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, clear sentence that directly states the tool's function without unnecessary words. It is front-loaded and efficiently conveys the core purpose, making it easy to parse quickly. There is no wasted language 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 simplicity (1 parameter, no annotations, no output schema), the description is minimal but inadequate. It does not cover parameter meaning, behavioral details like authentication or output format, or usage context compared to siblings. For a tool in a server with many sibling tools, more completeness is needed to guide effective use.

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 1 parameter with 0% description coverage, and the tool description does not mention any parameters. Since there is only one parameter, the baseline score is 4, but the description adds no value beyond the schema, so it is adjusted to 3. It fails to explain what 'projectId' represents or how to obtain it, which is a missed opportunity for clarity.

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 the README content of a GitHub project.' It specifies the verb ('Get') and resource ('README content of a GitHub project'), making it easy to understand what the tool does. However, it does not differentiate from sibling tools like 'get_project' or 'update_project_readme', which would require more specificity to score a 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, such as needing a project ID, or compare it to sibling tools like 'get_project' (which might return general project info) or 'update_project_readme' (for modifying the README). Without such context, users may struggle to select the correct 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get details', implying a read-only operation, but doesn't specify whether it requires authentication, rate limits, error conditions, or what details are returned. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 a single, clear sentence: 'Get details of a specific pull request'. There is zero waste, and every word contributes to the core purpose 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 complexity (a read operation with one parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't cover parameter semantics, behavioral traits like authentication needs, or return values. For a tool in this context, it should provide more guidance to be fully helpful.

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 description adds no meaning beyond the input schema. Schema description coverage is 0%, with one parameter 'pullNumber' undocumented. The description doesn't explain what 'pullNumber' is (e.g., an integer identifier), its format, or where to find it. It fails to compensate for the low 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: 'Get details of a specific pull request'. It uses a specific verb ('Get') and resource ('pull request'), and the qualifier 'specific' implies it retrieves one item by identifier. However, it doesn't explicitly differentiate from sibling tools like 'list_pull_requests' or 'update_pull_request', which would require a 5.

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

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

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention sibling tools like 'list_pull_requests' for multiple pull requests or 'update_pull_request' for modifications. It also lacks context on prerequisites, such as needing an existing pull request number.

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 'Get progress metrics' which implies a read operation, but it doesn't cover aspects like authentication needs, rate limits, error handling, or what the output looks like (e.g., metrics format, whether it's paginated). This is a significant gap for a tool with zero annotation coverage.

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 is front-loaded with the core purpose. There is no wasted text, and it's appropriately sized for a simple tool, making it highly 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?

Given the complexity (a metrics tool with 2 parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It doesn't explain return values, parameter details, or behavioral traits, making it inadequate for effective tool selection and invocation by an AI 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 input schema has 0% description coverage, so parameters 'sprintId' and 'includeIssues' are undocumented in the schema. The description adds no meaning beyond the tool's purpose; it doesn't explain what 'sprintId' should be (e.g., format, source) or what 'includeIssues' entails (e.g., whether it adds issue details to metrics). With low schema coverage, the description fails to compensate 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 'Get' and the resource 'progress metrics for a specific sprint', making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'get_current_sprint' or 'get_milestone_metrics', which might also retrieve sprint-related data, so it lacks sibling differentiation.

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. It doesn't mention prerequisites, context, or exclusions, such as how it differs from 'get_current_sprint' or other metrics tools in the sibling list, leaving usage unclear.

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 action but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, or what the output format looks like. This is a significant gap for a tool with parameters and no output schema.

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

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

The description is a single, efficient sentence with no wasted words. It is front-loaded with the core purpose, making it easy to scan and 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 complexity (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain return values, error conditions, or behavioral traits, leaving the agent with insufficient information to use the tool effectively beyond basic invocation.

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 0%, so the description must compensate by explaining parameters. It mentions 'within a time frame', which loosely relates to 'daysAhead', but doesn't clarify the meaning of 'limit' or 'includeIssues'. This adds minimal value beyond the bare 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 verb ('Get') and resource ('upcoming milestones'), specifying the scope ('within a time frame'). It distinguishes from siblings like 'list_milestones' by focusing on upcoming items, though it doesn't explicitly mention how it differs from 'get_overdue_milestones' or 'get_milestone_metrics'.

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 such as 'list_milestones' or 'get_overdue_milestones'. The description implies usage for time-based queries but lacks explicit context, prerequisites, 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?

With no annotations provided, the description carries full burden but only states the basic action without behavioral details. It doesn't disclose whether this is a read-only operation, what permissions are needed, how results are returned (pagination, format), or any rate limits. The description doesn't contradict annotations (none exist), but provides minimal 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 with zero wasted words. It's appropriately sized for a simple list operation and front-loads the core purpose immediately.

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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't explain what 'automation rules' are in this context, what information is returned, or how to interpret results. While concise, it lacks necessary context for effective tool selection and use.

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 0%, so the description must compensate but adds no parameter information. It mentions 'for a GitHub project' which hints at the 'projectId' parameter's purpose, but doesn't explain what format the ID should be, where to find it, or any constraints. With 1 undocumented parameter, the description provides insufficient semantic value.

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

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

The description clearly states the action ('List all automation rules') and the target resource ('for a GitHub project'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_automation_rule' (singular) or explain what distinguishes listing from getting individual rules.

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 about when to use this tool versus alternatives like 'get_automation_rule' or 'create_automation_rule'. The description mentions 'for a GitHub project' but doesn't specify prerequisites, context, or exclusions for 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 full burden for behavioral disclosure. While 'List all comments' implies a read-only operation, it doesn't disclose important behavioral aspects like pagination behavior (implied by the 'perPage' parameter), authentication requirements, rate limits, error conditions, or what 'all' means in practice (e.g., chronological order, filtering capabilities).

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 core purpose without unnecessary words. It's appropriately sized for a simple listing operation and gets straight to the point with zero waste.

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 2 parameters (one required), no annotations, no output schema, and 0% schema description coverage, the description is inadequate. It doesn't explain parameter usage, return format, pagination, error handling, or how it differs from related tools. The agent would struggle to use this effectively without additional 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?

With 0% schema description coverage for both parameters, the description provides no semantic information about 'issueNumber' (what format? repository context?) or 'perPage' (what values are valid? default behavior?). The description mentions 'all comments' which implies the tool returns comments, but doesn't explain how parameters affect this 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 tool's purpose with a specific verb ('List') and resource ('comments on a GitHub issue'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'get_issue' or 'list_issues' which might also provide comment information, so it doesn't reach the highest score.

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. With sibling tools like 'get_issue' (which might include comments) and 'create_issue_comment' (for adding comments), there's no indication of when this specific listing tool is preferred or what its scope limitations might be.

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 the full burden of behavioral disclosure. 'List GitHub issues' implies a read-only operation but does not specify critical traits like pagination behavior, rate limits, authentication requirements, error handling, or return format. For a tool with 7 parameters and no output schema, this is a significant gap in transparency.

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 just three words, making it front-loaded and waste-free. Every word ('List', 'GitHub', 'issues') contributes directly to the core purpose, though this brevity comes at the cost of completeness. It efficiently communicates the basic intent 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 complexity (7 parameters, 1 required), lack of annotations, and no output schema, the description is incomplete. It does not address parameter meanings, behavioral traits, or output expectations, leaving significant gaps for the agent to navigate. For a listing tool with multiple filtering options, more context is needed to be adequately helpful.

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 7 parameters with 0% description coverage, and the tool description provides no information about any parameters. It does not explain what 'status', 'milestone', 'labels', etc., mean, their expected formats, or how they affect the listing. With low schema coverage and no compensation in the description, this fails to add value beyond the bare 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 'List GitHub issues' clearly states the verb ('List') and resource ('GitHub issues'), providing a basic understanding of the tool's function. However, it lacks specificity about scope (e.g., repository, organization) and does not differentiate from sibling tools like 'get_issue' (which fetches a single issue) or 'list_project_items' (which might include issues). This makes it vague but not tautological.

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. It does not mention context such as filtering needs, comparison to 'get_issue' for single issues, or prerequisites like repository selection. Without any implied or explicit usage instructions, it leaves the agent to infer based on the tool name alone.

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 it lists labels but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or what 'all' entails (e.g., across repos or orgs). This is a significant gap for a list operation.

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. It's front-loaded and appropriately sized for a simple tool, making it easy 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 low complexity (one optional parameter) but lack of annotations and output schema, the description is incomplete. It doesn't explain return values, error handling, or scope, leaving gaps for an AI agent to use it correctly in context with siblings.

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 one parameter ('limit') with 0% description coverage. The description adds no information about parameters, so it doesn't compensate for the schema gap. However, with only one parameter, the baseline is moderate, but the lack of any param info keeps it at a minimal viable level.

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 ('List') and resource ('all GitHub labels'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like 'create_label' or other list tools, but it's specific enough to know what it does.

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. With siblings like 'list_issues' and 'list_milestones', there's no indication of context or prerequisites for listing labels specifically.

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 must fully disclose behavioral traits. 'List milestones' implies a read-only operation but offers no details on permissions, rate limits, pagination, error handling, or what the output contains. For a tool with parameters and no output schema, this lack of behavioral context is inadequate and leaves critical operational aspects unspecified.

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 only two words, making it front-loaded and free of unnecessary verbiage. While under-specified, it does not waste space on redundant or irrelevant information, adhering strictly to a minimalistic structure.

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, 1 required, no schema descriptions, no output schema, and no annotations), the description is severely incomplete. It does not address parameter meanings, behavioral expectations, output format, or differentiation from siblings. For a listing tool with filtering capabilities, this minimal description fails to provide the necessary context for effective use.

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 0%, meaning none of the three parameters (status, sort, direction) are documented in the schema. The description 'List milestones' adds no information about these parameters—it does not explain what 'status' represents, what sorting options exist, or how 'direction' affects results. This fails to compensate for the complete lack of schema 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 'List milestones' is a tautology that restates the tool name without adding meaningful context. It specifies the verb 'list' and resource 'milestones' but lacks any distinguishing details about scope, filtering, or how it differs from sibling tools like 'get_milestone_metrics', 'get_overdue_milestones', or 'get_upcoming_milestones'. This minimal statement fails to clarify what specific aspect of milestones is being listed.

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. It does not mention any context, prerequisites, or exclusions, nor does it reference sibling tools that might serve similar purposes (e.g., 'get_overdue_milestones' for filtered lists). Without such information, an agent cannot determine appropriate usage scenarios.

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 but only states it lists fields without disclosing behavioral traits like pagination, rate limits, error handling, or response format. It mentions 'all fields' but doesn't clarify if this includes archived or hidden fields, leaving gaps in transparency.

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 unnecessary words. It's front-loaded and appropriately sized for a simple list operation, earning full marks for conciseness.

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 (listing fields in a project), no annotations, no output schema, and low parameter coverage, the description is incomplete. It lacks details on return values, error cases, or operational constraints, making it inadequate for full agent 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 0%, but the description adds no parameter semantics beyond implying a 'projectId' is needed. It doesn't explain what 'projectId' refers to (e.g., numeric ID, name) or format requirements, so it doesn't compensate for the low coverage, resulting in a baseline score.

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 all fields') and resource ('in a GitHub project'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_field_value' or 'update_project_field', which prevents a score of 5.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_field_value' or 'create_project_field'. It lacks context about prerequisites, such as needing an existing project, or exclusions, leaving the agent with minimal usage direction.

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 the full burden of behavioral disclosure. It states it's a list operation, implying read-only behavior, but doesn't cover critical aspects like pagination (implied by 'limit' parameter), error handling, authentication needs, rate limits, or what the output looks like. This is inadequate for a tool with parameters and no output schema.

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

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

The description is a single, clear sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying 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 has 2 parameters (with 0% schema coverage), no annotations, no output schema, and multiple sibling list tools, the description is incomplete. It doesn't explain parameter semantics, behavioral traits, or differentiate from alternatives, leaving significant gaps for the agent to operate 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?

The input schema has 0% description coverage, so the description must compensate. It mentions 'GitHub project' which hints at 'projectId', but doesn't explain what 'projectId' is (e.g., a numeric ID or URL) or how 'limit' works (e.g., max items, default value). The description adds minimal value beyond the schema's structure.

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 'list' and the resource 'items in a GitHub project', making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_issues' or 'list_projects', which would require specifying what type of items are being listed (e.g., issues, pull requests, notes).

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 'list_issues' or 'list_projects'. There's no mention of prerequisites, context, or exclusions, leaving the agent to infer usage based on the tool name alone.

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