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 effectively describes the tool's behavior by stating the return type ('Created comment object'), supported features ('Supports: Markdown, mentions, references'), and providing an example. However, it lacks details on permissions, error conditions, or rate limits, which would be helpful 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 well-structured and front-loaded with the core purpose, followed by return value, usage guidelines, features, an example, and related tools. Each section is concise and adds value without redundancy, 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 moderate complexity (3 parameters, mutation operation) and lack of annotations or output schema, the description does a good job covering purpose, usage, and behavior. It includes an example and related tools, but could improve by mentioning authentication needs or potential side effects, which are relevant for a comment-adding tool.

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

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

The schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description does not add any parameter-specific information beyond what the schema provides, such as explaining the relationship between parameters or additional constraints. This meets the baseline for high schema coverage.

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

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

The description clearly states the action ('Add comment to issue') and resource ('issue'), distinguishing it from sibling tools like gitlab_add_merge_request_comment. It specifies the return value ('Created comment object'), making the purpose explicit and distinct.

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

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

The description explicitly provides usage context with 'Use when: Providing feedback, updates' and lists related tools ('gitlab_get_issue: Read issue first', 'gitlab_list_issues: Find issues'), offering clear guidance on when to use this tool and what alternatives to consider for prerequisite steps.

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 successfully adds valuable context beyond basic functionality: it specifies the return value ('Returns: Created comment object'), mentions supported features ('Supports: Markdown, mentions, slash commands'), and provides a concrete example. However, it doesn't mention permissions, rate limits, or error conditions that would be helpful 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 efficiently structured with clear sections: purpose statement, return value, usage guidance, supported features, example, and related tools. Every sentence earns its place by providing distinct, valuable information without redundancy. The information is front-loaded with the core functionality first.

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 parameters, 100% schema coverage, but no annotations or output schema, the description does a good job of providing context. It covers purpose, usage, return value, and supported features. However, as a write operation, it could benefit from mentioning authentication requirements or potential side effects that aren't covered by the schema.

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

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

The schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is high, but doesn't provide additional semantic context.

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 starts with a clear verb+resource statement 'Add comment to merge request' that precisely states what the tool does. It distinguishes this tool from sibling tools like gitlab_get_merge_request_notes (read existing) and gitlab_get_merge_request_discussions (threaded view), making the purpose specific and differentiated.

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

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

The description explicitly provides usage guidance with 'Use when: Code review feedback, discussions' and lists related tools with their specific purposes ('Read existing', 'Threaded view'). This gives clear context about when to use this tool versus alternatives, including both positive guidance (when to use) and implicit exclusions (when not to use).

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

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the action ('Approve'), a constraint ('Cannot approve your own MRs'), and the return value ('Approval status'). However, it lacks details on permissions required, error conditions, or rate limits, which would be helpful 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 efficiently structured with clear sections: purpose, returns, usage context, constraint, and related tools. Every sentence adds value without redundancy. It's front-loaded with the core action and appropriately sized for the tool's complexity.

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 (2 parameters, mutation operation) and no annotations or output schema, the description does well by covering purpose, usage guidelines, constraints, and alternatives. However, it could provide more behavioral context (e.g., what happens on success/failure, authentication requirements) to be fully complete for a mutation tool.

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

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

Schema description coverage is 100%, providing detailed documentation for both parameters. The description adds no parameter-specific information beyond what the schema already covers. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.

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

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

The description clearly states the specific action ('Approve a merge request') and resource ('merge request'), distinguishing it from sibling tools like 'gitlab_get_merge_request_approvals' (check status) and 'gitlab_merge_merge_request' (merge after approval). The verb 'approve' is precise and unambiguous.

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

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

The description explicitly states when to use ('Code review complete, changes approved') and when not to use ('Cannot approve your own MRs'), and provides clear alternatives ('gitlab_get_merge_request_approvals: Check status', 'gitlab_merge_merge_request: Merge after approval'). This gives comprehensive guidance for tool selection.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: atomic execution ('All-or-nothing execution'), rollback behavior ('Automatic rollback'), and result referencing ('Reference previous operation results'). However, it lacks details on permissions, rate limits, or error handling beyond the stop_on_error parameter, leaving some 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 well-structured with bullet points and an example workflow, making it easy to scan. However, it includes some redundancy (e.g., repeating 'automatic rollback' in multiple sections) and could be more front-loaded; the first sentence is clear, but subsequent details could be tighter.

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 3 parameters, 100% schema coverage, and no output schema, the description does a good job covering usage, behavior, and examples. It addresses atomicity and chaining, but lacks output details (e.g., result format) and could better explain error scenarios, given the absence of annotations.

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

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

Schema description coverage is 100%, so the schema documents parameters thoroughly. The description adds value by explaining the purpose of the 'operations' array ('Operation chaining', 'Result references') and contextualizing 'stop_on_error' in the example workflow, but doesn't significantly enhance parameter understanding beyond the schema's details.

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 ('Execute multiple operations atomically') and distinguishes it from siblings by highlighting its batch nature. It explicitly contrasts with 'Individual operation tools' and positions itself for 'Complex multi-step workflows,' making its unique role evident.

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

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

The description provides explicit guidance on when to use this tool ('Use when: Complex multi-step workflows') and when not to (by referencing 'Individual operation tools' for simpler cases). It also mentions an alternative tool ('gitlab_safe_preview_commit: Test first') for testing, offering clear context for selection.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: 'Returns: New commit on target branch' and 'Creates: New commit with same changes', clarifying it's a write operation that produces a new commit. However, it doesn't mention potential side effects like merge conflicts, permissions required, or error conditions, leaving some behavioral aspects uncovered.

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 well-structured and front-loaded with key information: purpose, returns, usage, and creation. Each section (e.g., 'Use when:', 'Example:', 'Related tools:') is concise and adds value without redundancy. The example is brief yet illustrative, making efficient use of space.

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 write operation with 3 parameters) and no annotations or output schema, the description does a good job covering purpose, usage, and behavior. However, it lacks details on error handling, authentication requirements, or what happens in edge cases (e.g., if the target branch doesn't exist), which would enhance completeness for a mutation tool.

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

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

Schema description coverage is 100%, providing detailed documentation for all three parameters (project_id, commit_sha, branch). The description adds minimal parameter semantics beyond the schema, only implying through the example that 'commit' maps to commit_sha and 'branch' maps to the target branch. It doesn't provide additional context or usage nuances for the parameters.

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

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

The description clearly states the specific action ('Apply commit to another branch') and resource ('commit'), distinguishing it from siblings like gitlab_create_commit (which creates new commits) and gitlab_get_commit (which retrieves commit info). The first sentence provides a precise verb+resource combination.

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

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

The description explicitly states 'Use when: Backporting fixes, selective changes' and provides a concrete example ('Example: Backport bug fix to stable'). It also lists related tools (gitlab_get_commit, gitlab_create_merge_request) to guide users toward alternatives or complementary actions, offering clear context for when 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the action ('Close merge request without merging'), the result ('Returns: Updated MR with closed state'), and important behavioral traits ('Note: Can be reopened later'). However, it doesn't mention potential side effects like notifications, permission requirements, or error conditions, leaving some 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 efficiently structured with clear sections: action, return value, usage context, important note, and related tools. Every sentence adds value without redundancy, and information is front-loaded with the core purpose stated first. The bulleted related tools section is particularly helpful for navigation.

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 does well by explaining the action, return value, usage context, and reversibility. However, it doesn't cover potential error cases, authentication requirements, or rate limits. Given the complexity of closing a merge request, some additional context about permissions or side effects would make it more complete.

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

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

The schema description coverage is 100%, with detailed explanations for both parameters (project_id and mr_iid). The description adds no parameter-specific information beyond what's already in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.

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

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

The description clearly states the specific action ('Close merge request without merging') and distinguishes it from sibling tools like 'gitlab_merge_merge_request' (for merging) and 'gitlab_update_merge_request' (for reopening or other updates). It explicitly identifies the resource (merge request) and the verb (close without merging), making the purpose unambiguous.

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

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

The description provides explicit guidance on when to use this tool ('Use when: Abandoning changes, deferring work') and clearly names alternatives ('gitlab_update_merge_request: Reopen or other updates' and 'gitlab_merge_merge_request: Merge instead'). This gives the agent clear context for selecting this tool over others in the sibling set.

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 does well by stating what the tool returns ('commits and diffs between refs') and showing what it does ('Shows: What changed between two points'). However, it doesn't mention potential limitations like rate limits, authentication requirements, or whether this is a read-only operation (though 'compare' implies non-destructive).

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

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

The description is perfectly structured and concise. It uses bullet points and clear sections ('Returns:', 'Use when:', 'Shows:', 'Example:', 'Related tools:') with zero wasted words. Every sentence earns its place by providing essential information in a scannable format.

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 comparison tool with no output schema and no annotations, the description does well by explaining what the tool returns and when to use it. However, it could be more complete by mentioning the output format (e.g., JSON structure of commits/diffs) or any limitations. The example helps, but without an output schema, more detail about the return value would be beneficial.

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

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

Schema description coverage is 100%, so the schema already fully documents all 4 parameters. The description adds minimal parameter semantics beyond the schema - it provides an example with 'from: "main"' and 'to: "feature/new-feature"' but doesn't explain parameter relationships or constraints beyond what's in the schema descriptions. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Compare two git references' with specific verbs and resources. It distinguishes from siblings by explicitly mentioning what it returns ('commits and diffs between refs') and provides a concrete example comparing 'feature branch to main', making the purpose unambiguous and distinct from related tools like gitlab_smart_diff.

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

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

The description explicitly states 'Use when: Reviewing changes before merge', providing clear context for when to use this tool. It also lists related tools with specific guidance: 'gitlab_create_merge_request: Create MR from comparison' and 'gitlab_smart_diff: Advanced diff options', giving clear alternatives for different use cases.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It does well by explaining key behavioral traits: atomic nature ('All changes or none'), operation limits ('Up to 100 operations'), supported action types, and return value ('Returns: New commit details'). However, it doesn't mention authentication requirements, rate limits, or error handling scenarios.

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 well-structured with clear sections (description, returns, use when, supports, key features, example, related tools) and front-loaded with the core purpose. While slightly longer than minimal, every section adds value. The example is particularly helpful but makes the description less concise than ideal.

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, 100% schema coverage, but no annotations or output schema, the description does quite well. It covers purpose, usage context, behavioral traits, and provides a concrete example. The main gap is the lack of output details (only 'New commit details' is mentioned without structure), but given the complexity and schema richness, this is reasonably complete.

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

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

With 100% schema description coverage, the baseline is 3. The description adds minimal parameter semantics beyond the schema - it mentions 'Multiple files: Up to 100 operations' which aligns with the schema's max items constraint, and provides an example showing the actions array structure. However, it doesn't add significant meaning beyond what's already documented in the comprehensive schema descriptions.

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

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

The description clearly states the tool's purpose with specific verbs ('Create commit with file changes') and distinguishes it from siblings by mentioning atomic operations and multiple file capabilities. It explicitly differentiates from gitlab_safe_preview_commit and gitlab_list_repository_tree in the 'Related tools' section.

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

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

The description provides explicit usage guidance with 'Use when: Making changes via API' and offers clear alternatives in the 'Related tools' section (gitlab_safe_preview_commit for previewing first, gitlab_list_repository_tree for checking existing files). This gives the agent specific when-to-use and when-not-to-use 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. It clearly indicates this is a creation/mutation tool ('Creates: New snippet'), specifies required vs. optional parameters, describes the return value ('Returns: Created snippet with ID and URLs'), and provides an example. It doesn't mention authentication needs, rate limits, or error conditions, but covers the essential behavioral aspects well for a creation 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 well-structured with clear sections (purpose, creates, use when, required/optional parameters, example, returns, related tools). Every sentence adds value - there's no redundant information, and the example is appropriately brief yet illustrative.

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 no annotations and no output schema, the description does an excellent job covering purpose, usage, parameters, and expected return. It provides an example and related tool guidance. The main gap is the lack of output schema, but the description compensates by describing the return value. It doesn't cover error cases or authentication requirements, which keeps it from a perfect score.

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

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

Schema description coverage is 100%, so the schema already fully documents all 6 parameters. The description lists required and optional parameters but doesn't add meaningful semantic context beyond what's in the schema (e.g., it doesn't explain relationships between parameters or provide additional usage nuances). This meets the baseline for high schema coverage.

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

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

The description clearly states the specific action ('Create a new code snippet') and resource ('snippet with specified content and metadata'), distinguishing it from sibling tools like gitlab_update_snippet (modify) and gitlab_list_snippets (view). The opening line is direct and unambiguous.

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

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

The description explicitly provides 'Use when' scenarios (saving reusable code, sharing solutions, documenting examples) and lists 'Related tools' with specific guidance on when to use alternatives (modify after creation, view created snippets). This gives clear context for when to choose this tool over others.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: that it returns metadata only (not actual file downloads), mentions security implications, lists what content is returned (artifacts with sizes and expiration), and provides a detailed example response. The only minor gap is lack of explicit mention about whether this is a read-only 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 well-structured and efficiently organized with clear sections (Returns, Use when, Security, Content, Example response, Related tools). Every sentence earns its place, providing essential information without redundancy. The information is front-loaded with the core purpose immediately stated.

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 and no output schema, the description provides substantial context: clear purpose, usage guidelines, behavioral details, security notes, example response, and related tools. The main gap is the lack of explicit output schema documentation, though the example response partially compensates. Given the complexity and lack of structured fields, this is quite comprehensive.

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

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

With 100% schema description coverage, the schema already comprehensively documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions, so it meets the baseline expectation without providing additional 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 tool's purpose with specific verbs ('Get information about job artifacts') and distinguishes it from actual file downloads. It explicitly mentions what it returns ('Artifact metadata and download information') and differentiates from sibling tools like gitlab_list_pipeline_jobs by focusing on artifact details rather than job listing.

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

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

The description provides explicit usage guidance with a 'Use when:' section listing specific scenarios (checking build outputs, downloading test results, accessing reports). It also references related tools for finding job IDs and browsing jobs, clearly establishing when to use this tool versus 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. It discloses that the tool returns 'Complete commit information with stats' and includes an example response showing the structure and content. However, it doesn't mention error conditions, rate limits, or authentication requirements, which are typical behavioral traits for API tools.

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 well-structured and front-loaded with key information (purpose, returns, usage, required parameter). Each section ('Returns:', 'Use when:', 'Required:', 'Example response:', 'Related tools:') adds value without redundancy. The example response is illustrative but not overly verbose.

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

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

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description provides good context: purpose, usage guidelines, example response, and related tools. It covers most needs for a read operation, though it lacks details on error handling or authentication, which would be helpful for completeness.

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

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

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema, only noting 'Required: Commit SHA (short or full)' which is already in the schema. No additional semantic context is provided for parameters like 'project_id' or 'include_stats'.

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 ('Get') and resource ('single commit details'), distinguishing it from siblings like 'gitlab_list_commits' (browse history) and 'gitlab_get_commit_diff' (view changes). The opening line 'Get single commit details' is precise and unambiguous.

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

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

The description explicitly states 'Use when: Examining specific commit' and provides a 'Related tools' section that names alternatives (e.g., 'gitlab_list_commits: Browse history'), giving clear guidance on when to use this tool versus others. This helps the agent select the right tool for the task.

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 describes what the tool returns ('Detailed diff of all changed files') and shows an example response format, which is helpful. However, it doesn't mention important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or error conditions. The example response adds value but doesn't fully compensate for the lack of annotations.

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

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

The description is well-structured and efficiently organized with clear sections: purpose statement, returns, use cases, shows, example response, and related tools. Every sentence earns its place, and the information is front-loaded with the most important details first. The example response is appropriately included to illustrate the output format.

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 that there's no output schema, the description provides a good example response that shows the structure of the returned data. The tool has 2 parameters with 100% schema coverage, and the description adds context about when to use it and what it returns. However, for a tool with no annotations, it could provide more behavioral context about safety, permissions, or limitations to be fully complete.

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

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

The schema description coverage is 100%, meaning both parameters are well-documented in the schema itself. The description doesn't add any additional parameter information beyond what's already in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

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 commit diff/changes' followed by 'Returns: Detailed diff of all changed files'. This specifies both the action (get) and resource (commit diff/changes), and the 'Returns' statement clarifies the output. However, it doesn't explicitly differentiate from sibling tools like 'gitlab_smart_diff' beyond listing it as related.

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 includes a 'Use when:' section that provides clear context: 'Code review, understanding changes'. This gives practical guidance on when this tool is appropriate. However, it doesn't explicitly state when NOT to use it or provide direct comparisons with alternatives like 'gitlab_smart_diff' beyond listing it as related.

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

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

With no annotations provided, the description carries the full burden. It explains the multi-step process: checks git remotes, identifies GitLab URLs, fetches project details from API. It also states 'No parameters needed' (though there is an optional path parameter) and mentions what it returns. However, it doesn't disclose error handling, authentication requirements, or rate limits.

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 well-structured with clear sections (Returns, Use when, No parameters needed, How it works, Related tools), front-loads key information, and every sentence earns its place. It's appropriately sized for the tool's complexity without unnecessary verbosity.

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 (auto-detection logic), no annotations, and no output schema, the description does well by explaining the detection process, usage context, and return value reference. However, it could better address the optional path parameter discrepancy and provide more detail on error cases or authentication requirements.

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

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

Schema description coverage is 100%, so the schema already fully documents the single optional 'path' parameter. The description adds minimal value beyond the schema: it mentions 'Examines git remotes' and 'No parameters needed' (contradicted by the schema's optional path), but doesn't provide additional semantic context about parameter usage or implications.

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: 'Auto-detect project from git repository' - a specific verb ('auto-detect') and resource ('project from git repository'). It distinguishes from siblings by explaining it examines git remotes automatically, unlike gitlab_get_project which requires a known project ID.

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

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

The description provides explicit usage guidance: 'Use when: Working in a git repo with GitLab remote' and lists two related tools with clear alternatives: gitlab_get_project for when you know the project ID, and gitlab_list_projects for browsing available projects. This gives clear when-to-use and when-not-to-use 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?

No annotations are provided, so the description carries the full burden. It implies a read-only operation by using 'Get' and lists return fields, but does not disclose behavioral traits like authentication requirements, rate limits, error handling, or whether it's idempotent. The example response adds some context but lacks 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 well-structured with a clear purpose statement, bulleted details, use cases, and an example, all in a compact format. Every sentence adds value, but it could be slightly more concise by integrating the example into the bullet points or reducing redundancy in the use cases.

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

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

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is quite complete. It explains what the tool does, what it returns, and when to use it. However, it lacks some behavioral context (e.g., authentication needs, error cases), which prevents a perfect score despite the low complexity.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics by listing return fields and providing an example, adding value beyond the schema. A baseline of 4 is given since no parameters exist, and the description compensates well for the lack of output 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 tool's purpose with a specific verb ('Get') and resource ('currently authenticated user's profile'), distinguishing it from sibling tools like 'gitlab_get_user' or 'gitlab_get_my_profile' by specifying it returns the authenticated user's own data. The detailed bullet points reinforce what information is retrieved.

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

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

The description provides explicit use cases (e.g., 'Verify authentication is working', 'Get user context for automation scripts'), which offer clear guidance on when to use this tool. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as 'gitlab_get_user' for other users' data.

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

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

With no annotations provided, the description carries the full burden. It discloses the return type ('Raw file content as string') and default behavior ('defaults to default branch'), but doesn't mention error conditions, rate limits, authentication requirements, or what happens with binary files. It provides basic behavioral context but lacks comprehensive 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 well-structured and front-loaded with core purpose and return value. Each section ('Returns:', 'Use when:', 'Optional:', 'Example:', 'Related tools:') adds specific value without redundancy. Every sentence earns its place in helping the agent understand the 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?

For a read-only tool with 100% schema coverage but no output schema, the description provides good context about usage scenarios, examples, and related tools. However, without annotations or output schema, it could better address error cases or limitations. The completeness is strong but not perfect given the complexity.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond the schema - it mentions the optional ref parameter defaults to default branch, which is already in the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Get file content from repository') and resource ('file'), distinguishing it from siblings like gitlab_list_repository_tree (browsing) and gitlab_create_commit (modifying). The examples further clarify the purpose by showing different file types.

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?

Explicit guidance is provided with 'Use when: Reading source code, configs, documentation' and 'Related tools' section that names alternatives for browsing files, modifying files, and seeing changes. This gives clear context for when to use this tool versus other options.

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 discloses that the tool returns 'complete group metadata, settings, statistics' and includes an example response, which adds context about output format. However, it doesn't mention behavioral traits like rate limits, authentication needs, or error handling, leaving some gaps 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 well-structured with sections like 'Returns', 'Use when', 'Optional', and 'Related tools', making it easy to scan. Each sentence adds value without waste, such as clarifying usage and providing an example, resulting in an efficient and front-loaded presentation.

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

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

Given no annotations and no output schema, the description compensates well by including an example response and usage guidelines. It covers the tool's purpose, when to use it, and related tools, but lacks details on error cases or performance limits, which could be useful for a read operation with no structured output schema.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value by mentioning 'with_projects=true' includes 'first page of projects', but this is largely redundant with the schema's detailed description. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the verb 'Get' and resource 'detailed group information', specifying it returns 'complete group metadata, settings, statistics'. It distinguishes from siblings like 'gitlab_list_groups' (browse groups) and 'gitlab_list_group_projects' (list projects), making the purpose specific and differentiated.

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

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

The 'Use when' section explicitly states scenarios: 'Need full group details, checking configuration, counting projects'. It also names related tools as alternatives, providing clear guidance on when to use this tool versus others, such as for detailed info rather than browsing or listing projects.

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 effectively describes the tool's behavior by specifying what it returns ('Full issue data including description, comments count'), providing a detailed example output, and explaining the IID parameter. However, it doesn't mention potential errors, rate limits, or authentication needs, leaving some behavioral aspects uncovered.

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 well-structured with clear sections (e.g., 'Returns:', 'Use when:', 'Required:', 'What's IID?:', 'Example:', 'Related tools:'), making it easy to scan. It's appropriately sized, though the detailed example output is lengthy but informative. Every sentence adds value, with no 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?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is quite complete. It covers purpose, usage, parameters, and example output, but lacks details on error handling, authentication, or rate limits. The absence of an output schema is mitigated by the example return structure, though not fully.

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

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

The schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the 'issue_iid' parameter in detail ('What's IID?: Internal ID - the issue number shown in GitLab') with examples, clarifying its semantics beyond the schema. However, it doesn't provide similar elaboration for 'project_id', which is auto-detected but could benefit from more context.

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

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

The description clearly states the tool's purpose with specific verbs ('Get complete issue details') and resources ('issue'), and explicitly distinguishes it from sibling tools like 'gitlab_list_issues' (for finding issues) and 'gitlab_update_issue' (for modifying issues). The title 'Get complete issue details' reinforces this specificity.

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

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

The description provides explicit usage guidance with a 'Use when:' section ('Need complete issue information'), lists required parameters ('Required: Issue IID'), and names related tools with their purposes (e.g., 'gitlab_list_issues: Find issues'). This clearly indicates when to use this tool versus 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. It discloses key behavioral traits: it returns full MR data including pipelines, approvals, and conflicts, and specifies required parameters (MR IID). However, it doesn't mention rate limits, authentication needs, or error handling, leaving some gaps for a read 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 well-structured and front-loaded with key information (purpose, returns, usage), followed by helpful details (parameter explanation, example, related tools). Every sentence earns its place without redundancy, making it efficient and easy to scan.

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 (read operation with 2 parameters) and no output schema, the description provides a comprehensive example return structure, which compensates well. It covers purpose, usage, parameters, and sibling relationships, though it lacks details on authentication or error cases, which are minor gaps.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema by explaining 'What's IID?' and providing an example, but doesn't add significant semantic context. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('Get complete merge request details') and resource ('merge request'), and distinguishes it from siblings by listing related tools like gitlab_get_merge_request_changes for diffs and gitlab_get_merge_request_discussions for reviews. This provides precise differentiation beyond just the name.

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

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

The description explicitly states when to use it ('Use when: Reviewing MR, checking merge status') and lists related tools as alternatives for specific needs (e.g., 'gitlab_get_merge_request_changes: See actual diffs'). This gives clear guidance on context and when to choose other tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly indicates this is a read operation ('Check', 'Returns', 'Shows') and provides a detailed example response showing the data structure. However, it doesn't mention potential errors, rate limits, or authentication requirements.

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

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

The description is perfectly structured with clear sections: purpose, returns, usage, example response, and related tools. Every sentence earns its place, and the information is front-loaded with the core purpose first.

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 read-only tool with no output schema, the description provides excellent context including a detailed example response. It covers purpose, usage, and relationships to other tools. The main gap is the lack of error handling or edge case information, but otherwise it's quite complete.

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

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

The schema description coverage is 100%, providing comprehensive documentation for both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with a specific verb ('Check') and resource ('MR approval status'), and distinguishes it from siblings by showing what it returns. It explicitly differentiates from gitlab_approve_merge_request and gitlab_merge_merge_request, making its read-only nature clear.

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

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

The description provides explicit usage guidance with 'Use when: Checking if MR can be merged' and lists related tools with their purposes. This gives clear context for when to use this tool versus alternatives like approving or merging.

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

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns 'Complete diffs for all files', 'Full file diffs with context', and includes 'all commits in the MR'. However, it doesn't mention potential limitations like rate limits, authentication needs, or pagination behavior, which would be helpful for a read 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 well-structured with bullet-like sections (Returns, Use when, Shows) and a related tools list. Every sentence adds value: the first line states the purpose, subsequent lines provide context and comparisons, and the final section clarifies sibling relationships. No wasted words.

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

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

Given the tool's moderate complexity (read operation with 2 parameters), no annotations, and no output schema, the description does a good job explaining what the tool returns ('Complete diffs', 'Full file diffs with context') and when to use it. However, it could benefit from more detail on output format or error conditions to be fully complete.

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

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

Schema description coverage is 100%, providing detailed documentation for both parameters. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline of 3. It doesn't compensate but doesn't need to given the comprehensive 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 tool's purpose with specific verbs ('Get detailed MR file changes') and resources ('MR file changes', 'Complete diffs for all files'). It effectively distinguishes from siblings like 'gitlab_get_merge_request' (overview) and 'gitlab_smart_diff' (customizable diffs) by specifying it returns complete diffs for all files in the MR.

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

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

The description provides explicit usage guidance with 'Use when: Reviewing code changes' and lists related tools with clear distinctions ('gitlab_get_merge_request: MR overview', 'gitlab_smart_diff: Customizable diffs'). This tells the agent precisely when to use this tool versus 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. It discloses key behavioral traits: it returns threaded discussions with replies (output format), and the example response shows structure including resolvable status. However, it doesn't mention pagination behavior, rate limits, or authentication requirements, leaving some 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 well-structured with purpose, usage guidance, example, and related tools sections. It's appropriately sized, but the example response is quite detailed and could be summarized more concisely. Most sentences earn their place, though some formatting could be tighter.

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

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

Given no annotations and no output schema, the description does a good job explaining what the tool returns (threaded discussions with replies) and when to use it. The example response provides concrete output structure. However, it doesn't cover error cases, authentication needs, or pagination behavior, which would be helpful for a read 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 100%, so the schema fully documents all parameters. The description adds no parameter-specific information beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.

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

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

The description clearly states the tool's purpose with specific verb ('Get') and resource ('MR discussion threads'), and distinguishes it from sibling tools like 'gitlab_get_merge_request_notes' by emphasizing thread structure. The opening line 'Get MR discussion threads' is direct and unambiguous.

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

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

The description explicitly provides usage guidance with 'Use when: Reading code review comments' and 'Better than notes: Shows thread structure', which differentiates it from the 'gitlab_get_merge_request_notes' sibling tool. It also lists related tools for follow-up actions, offering clear 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. It effectively describes key traits: it's a read operation (implied by 'List'), includes pagination details ('Yes (default 10 per page)'), sorting options ('By created_at or updated_at'), and provides an example response structure. However, it doesn't mention rate limits, authentication needs, or error handling, leaving some gaps for a tool with 7 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 well-structured and front-loaded: it starts with the core purpose, followed by key behavioral details (returns, use when, pagination, sorting), an example response, and related tools. Each section is brief and informative, with no wasted sentences. The formatting (bulleted lists in the response) enhances readability without verbosity.

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

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

Given the tool's complexity (7 parameters, no annotations, no output schema), the description is largely complete: it covers purpose, usage, pagination, sorting, and provides an example response. However, it lacks details on error cases, rate limits, or authentication requirements, which are important for a read operation in a GitLab context. The example response helps but doesn't fully substitute for an output schema.

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

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

The schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds no specific parameter semantics beyond what's in the schema (e.g., it doesn't explain how 'max_body_length' interacts with the example response). This meets the baseline of 3 since the schema does the heavy lifting, but the description doesn't compensate with additional insights.

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 starts with a clear verb ('List') and resource ('merge request comments'), specifying the exact scope. It distinguishes from sibling tools like 'gitlab_get_merge_request_discussions' by noting it returns an array of notes/comments, not threaded discussions, and from 'gitlab_add_merge_request_comment' by being a read operation. This is specific and avoids tautology.

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

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

The description explicitly states 'Use when: Reading MR discussions, reviews', providing clear context for when to invoke this tool. It also lists related tools with brief distinctions (e.g., 'gitlab_get_merge_request_discussions: Threaded discussions'), guiding the agent on alternatives. This offers explicit guidance on when to use this tool versus others.

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

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

With no annotations provided, the description carries full burden and does well by disclosing authentication requirements ('uses authentication token'), privacy aspects ('private settings', 'not available via public APIs'), and the comprehensive nature of returned data. It doesn't mention rate limits or error conditions, but covers key behavioral traits.

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?

Perfectly structured with clear sections: purpose statement, detailed return data breakdown, use cases, and parameter clarification. Every sentence adds value with zero waste, 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 0-parameter tool with no output schema, the description provides excellent context about what data is returned, use cases, and authentication. It could potentially mention response format or error handling, but given the tool's simplicity, it's quite complete.

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

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

The schema has 0 parameters with 100% coverage, so baseline would be 3. The description adds value by explicitly stating 'No parameters required - uses authentication token', which clarifies the authentication mechanism beyond what the empty schema indicates.

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', 'Retrieve') and resource ('current authenticated user's complete profile') with specific scope. It distinguishes from siblings like 'gitlab_get_user' by emphasizing it's for the authenticated user's own profile with private data not available via public APIs.

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 'Use cases' section provides clear context for when to use this tool (dashboard display, account verification, statistics, data export). However, it doesn't explicitly state when NOT to use it or mention alternatives like 'gitlab_get_user' for other users' public profiles.

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 the tool returns 'Complete project metadata, settings, statistics' and provides an example response, which adds valuable behavioral context. However, it doesn't mention potential limitations like authentication requirements, rate limits, or error conditions.

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 well-structured with clear sections (purpose, returns, usage, required, example, related tools) and front-loaded key information. It's appropriately sized, though the example response is detailed but necessary for clarity. Every sentence earns its place.

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

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

Given the tool's low complexity (1 parameter, no nested objects) and 100% schema coverage, the description is mostly complete. It provides purpose, usage guidelines, example output, and sibling differentiation. However, with no output schema and no annotations, it could benefit from more behavioral details like error handling or authentication needs.

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

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

Schema description coverage is 100%, so the schema already documents the 'project_id' parameter thoroughly with examples and format details. The description adds minimal value beyond stating 'Required: Project ID or path', which is already covered in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the verb 'Get' and resource 'detailed project information', making the purpose specific. It distinguishes from siblings like 'gitlab_list_projects' (find projects) and 'gitlab_get_current_project' (auto-detect), establishing clear 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 explicitly provides usage guidance with 'Use when: Need full project details, checking configuration' and lists related tools with their purposes. It clearly indicates when to use this tool versus alternatives like 'gitlab_list_projects' for finding projects.

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

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns 'Complete snippet information with content' and includes an example response showing the structure. However, it doesn't mention potential errors (e.g., if snippet_id is invalid), rate limits, or authentication needs, leaving some 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 well-structured with sections like 'Returns', 'Use when', 'Content', and 'Related tools', making it easy to scan. It includes an example response, which is helpful but adds length. Some redundancy exists (e.g., 'Content: Full text content included' could be merged), but overall it's efficient.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is fairly complete. It explains the purpose, usage, and output via an example. However, it lacks details on error handling or authentication, which would be beneficial for full contextual understanding.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter information beyond what the schema provides (e.g., no clarification on project_id auto-detection or snippet_id sourcing). This meets the baseline of 3 when schema coverage is high.

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 snippet details and content') and resource ('snippet'), distinguishing it from siblings like gitlab_list_snippets (browsing) and gitlab_update_snippet (modifying). It explicitly mentions what is returned ('Complete snippet information with content'), making the purpose unambiguous.

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

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

The description includes an explicit 'Use when' section ('Reading snippet code, reviewing implementations'), providing clear context for when to invoke this tool. It also lists related tools with brief descriptions (e.g., gitlab_list_snippets for browsing, gitlab_update_snippet for modifying), offering alternatives and differentiation.

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 discloses that the tool returns 'essential user details like name, username, avatar, and public profile info,' which clarifies the scope and output. However, it doesn't mention rate limits, authentication requirements, or error conditions, leaving some behavioral 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?

Well-structured with clear sections: purpose, return values, usage guidelines, parameters, examples, and alternatives. Every sentence adds value without redundancy, and key information is front-loaded (e.g., purpose and when-to-use in the first two sentences).

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

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

Given no annotations, no output schema, and a simple read operation with 2 parameters, the description is mostly complete. It covers purpose, usage, parameters, and alternatives effectively. However, it lacks details on output format or potential errors, which could be helpful for an agent, though not critical for this tool's simplicity.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds value by clarifying the mutual exclusivity ('Use either user_id OR username, not both') and providing usage examples, but doesn't add significant semantic details beyond what's in the schema. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the specific action ('Get basic profile information'), target resource ('for a specific GitLab user'), and method ('by ID or username'). It distinguishes from siblings like 'gitlab_search_user' by emphasizing exact identification versus partial search.

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

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

Explicitly states when to use ('when you have a specific user ID or exact username and need basic profile information') and when not to use ('For searching users with partial information, use 'gitlab_search_user' instead'). It also mentions alternatives for more comprehensive data ('For comprehensive user activity and contributions, use user activity tools instead').

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 describes the return format in detail (event details, timestamps, project context, etc.) and mentions pagination via per_page and page parameters, which adds useful context. However, it does not cover critical aspects like authentication requirements, rate limits, error handling, or whether it's read-only/destructive, leaving gaps in transparency for a tool with 8 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 well-structured with sections for purpose, returns, use cases, parameters, and an example, making it easy to scan. It is appropriately sized for an 8-parameter tool, but some parts (like the detailed return list) could be slightly condensed without losing clarity, keeping it from a perfect score.

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 (8 parameters, no annotations, no output schema), the description is moderately complete. It covers purpose, returns, use cases, and parameters adequately, but lacks details on authentication, error handling, rate limits, and exact output structure, which are important for a retrieval tool with filtering options. This leaves room for improvement in contextual coverage.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by clarifying parameter usage: it explains that 'user_id or username' can be used interchangeably, provides an example with specific values, and lists all parameters with brief context. This enhances understanding beyond the schema, though it doesn't add deep semantic details like enum values or complex constraints.

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 ('Retrieve user's complete activity/events timeline') and resource ('user activities including commits, issues, MRs, comments, and other interactions across all accessible projects'). It distinguishes from siblings like gitlab_list_user_events by emphasizing 'complete activity/events timeline' with detailed return structure, making the purpose specific and well-differentiated.

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

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

The description provides clear use cases ('Track user engagement patterns', 'Monitor team member activities', etc.) that implicitly guide when to use this tool. However, it lacks explicit alternatives or exclusions, such as when to prefer gitlab_list_user_events or other sibling tools for simpler event listings, which prevents a perfect score.

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 discloses the tool's behavioral traits by describing what it returns (code change summary with specific metrics) and implies it's a read-only analysis tool. However, it doesn't mention rate limits, authentication requirements, pagination behavior (though 'per_page' is in schema), or whether it's computationally expensive for large date ranges.

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 front-loaded with the core purpose, but contains some redundancy (e.g., 'Get lines added/removed' then 'Generate comprehensive statistics'). The 'Use cases' section is helpful but could be more concise. The example is valuable but lengthy. Overall, it's somewhat verbose but well-structured with clear sections.

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 5 parameters with 100% schema coverage but no annotations and no output schema, the description does a good job explaining what the tool returns and its use cases. It provides substantial context about the analysis capabilities and metrics. However, it doesn't fully compensate for the lack of output schema by detailing the exact structure of returned data, and there's parameter inconsistency between description and schema.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds significant value by explaining parameter semantics beyond the schema: it clarifies that 'user_id' and 'username' are alternatives ('use either'), explains what 'include_languages' and 'include_trends' do, defines 'granularity' options, and provides a comprehensive example. However, it mentions parameters not in the schema (user_id, include_languages, include_trends, granularity), creating some inconsistency.

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 lines added/removed and files changed by user over period' and 'Generate comprehensive statistics about a user's code contributions'. It distinguishes from siblings like 'gitlab_get_user_commits' (which lists commits) and 'gitlab_get_user_contributions_summary' (which might be broader) by focusing specifically on code change metrics with quantitative analysis.

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

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

The description provides clear usage context with 'Use cases' section listing development productivity analysis, code contribution reporting, team capacity planning, and performance review data. It also includes an example showing typical parameter usage. However, it doesn't explicitly state when NOT to use this tool or name specific alternatives among 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 the full burden of behavioral disclosure. It effectively describes the tool's behavior by detailing what it returns (commit details, code changes, context, author info, statistics), mentions pagination behavior via 'per_page' and 'page' parameters, and specifies filtering capabilities. However, it doesn't explicitly mention rate limits, authentication requirements, or potential side effects, leaving some behavioral aspects uncovered.

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

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

The description is front-loaded with the core purpose and usage guidance, but it becomes repetitive with multiple sections (e.g., 'Use cases' and 'Examples' overlap, and the parameter list duplicates schema info). Some sentences, like the second bullet under 'Use cases', could be condensed or removed to improve efficiency without losing value.

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, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, behavior, and parameters, but lacks details on output format (only lists return categories without structure) and doesn't address potential errors or limitations. It compensates well for the absence of annotations and output schema, but could be more thorough.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by listing parameters with brief explanations and providing an example, but it doesn't add significant semantic context or clarify interdependencies (e.g., 'user_id' vs. 'username'). Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('List all commits authored by a specific user') and resources ('commits', 'user'), and distinguishes it from sibling tools by explicitly mentioning 'gitlab_get_user_merge_commits' as an alternative for merge commits. The bolded sentence reinforces the core purpose.

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

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

The description provides explicit guidance on when to use this tool vs. alternatives, stating 'For merge commits specifically, use 'gitlab_get_user_merge_commits' instead.' It also lists multiple use cases (e.g., code contribution analysis, developer productivity metrics) and includes an example, giving clear context for application.

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 describes the tool's function well and lists what data is returned, but doesn't mention potential limitations like rate limits, authentication requirements, data freshness, or error conditions. It adds value by specifying the aggregation scope but lacks 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 well-structured with clear sections (purpose, returns, use cases, parameters, example) and avoids unnecessary repetition. However, the 'Returns contribution summary including' section is somewhat verbose and could be more concise while maintaining clarity.

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 read-only summary tool with 5 parameters and 100% schema coverage but no output schema, the description provides good context: it clearly explains what the tool does, what data it returns, use cases, and includes an example. The main gap is the lack of output schema, which the description partially compensates for by listing return data categories.

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

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

The schema description coverage is 100%, so the schema already documents all parameters. The description adds minimal value beyond the schema by mentioning 'use either user_id or username' and providing an example, but doesn't explain parameter interactions, defaults, or constraints beyond what's in the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Summarize user's recent contributions across issues, MRs, and commits' with a specific verb ('summarize') and resource ('user's contributions'). It distinguishes itself from siblings like gitlab_get_user_commits or gitlab_get_user_activity_feed by focusing on aggregated, multi-source summaries rather than individual activity streams.

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

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

The description provides clear context for when to use this tool through 'Use cases' (performance reviews, team tracking, identifying contributors, project monitoring). However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools, such as gitlab_get_user_activity_feed for raw activity data instead of summaries.

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 effectively describes what the tool returns ('detailed information about a user's GitLab activity'), including specific categories like profile, statistics, activity, settings, and social links. However, it lacks details on error handling, rate limits, or authentication requirements, which would elevate the score further.

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

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

The description is front-loaded with the core purpose but becomes verbose with redundant sections like 'Use cases' and 'Returns extended user information' that repeat earlier points. While informative, it could be more streamlined by eliminating repetition, such as merging the activity summary with the detailed return list.

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 user details tool with no annotations and no output schema, the description does a good job of covering purpose, usage, and return values. It includes examples, parameter guidance, and sibling tool differentiation. However, it could improve by specifying output format or error cases to be fully comprehensive.

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

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

Schema description coverage is 100%, so the schema already documents both parameters (user_id and username). The description adds minimal value beyond the schema by noting 'use either user_id or username' and providing an example, but it doesn't clarify exclusivity or priority rules. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('Get comprehensive activity summary and contributions') and resources ('for a specific user'). It explicitly distinguishes itself from sibling tools like 'gitlab_get_user' for basic profile info and 'gitlab_search_user' for finding users, making the differentiation clear and actionable.

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

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

The description provides explicit guidance on when to use this tool ('when you need detailed insights into a user's GitLab activity and contributions') and when to use alternatives ('For basic user profile info, use 'gitlab_get_user' instead. For finding users by search, use 'gitlab_search_user' instead.'). It also includes use cases and examples, offering comprehensive context for 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. It discloses that the tool returns discussion thread information with details like engagement, origin, timeline, and impact, which adds behavioral context beyond basic functionality. However, it does not mention important behavioral traits such as authentication requirements, rate limits, error handling, or whether this is a read-only operation (though 'Get' implies it).

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

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

The description is well-structured with clear sections (purpose, returns, use cases, parameters, example) and is appropriately sized. Most sentences earn their place by adding value, though the 'Returns' section could be more concise. It is front-loaded with the core purpose, making it easy to understand quickly.

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

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

Given the complexity (5 parameters, no output schema, no annotations), the description is fairly complete. It explains what the tool does, provides use cases, details parameters, and includes an example. However, it lacks information on output format, pagination behavior beyond parameters, and error scenarios, which would enhance completeness for a tool with no output schema.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description lists parameters with brief explanations (e.g., 'user_id: Numeric user ID', 'username: Username string (use either user_id or username)'), but these add minimal value beyond what's in the schema. The example provides some usage context, but overall, the description does not significantly enhance parameter understanding beyond the schema's comprehensive coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('Get all discussion threads started by a user') and resource ('discussion threads initiated by the specified user across issues, merge requests, and other collaborative contexts'). It distinguishes itself from sibling tools like gitlab_get_user_issue_comments or gitlab_get_user_mr_comments by focusing on threads started by the user rather than comments or other contributions.

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

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

The description provides clear context for when to use this tool ('Leadership and initiative tracking', 'Communication effectiveness analysis', etc.), but does not explicitly state when not to use it or name specific alternatives among the sibling tools. It implies usage through use cases but lacks explicit exclusions or comparisons to similar tools like gitlab_get_user_resolved_threads.

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 the scope ('across all accessible projects'), time coverage ('all time periods'), and return format details (comment details, issue info, etc.). However, it doesn't mention pagination behavior (implied by parameters but not explained), rate limits, or authentication requirements.

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 well-structured with purpose, scope, return details, use cases, parameters, and example. However, it could be more front-loaded - the parameter list is extensive and might bury key information. Every sentence adds value, but some redundancy exists between parameter descriptions and schema.

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 6-parameter tool with no annotations and no output schema, the description provides good context about what the tool does and returns. However, it lacks behavioral details like pagination mechanics, error conditions, or performance characteristics that would help an agent use it effectively.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description lists parameters with brief explanations but doesn't add significant meaning beyond what's in the schema (e.g., it doesn't clarify parameter interactions like 'use either user_id or username' when user_id isn't in the schema). Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('Get', 'Retrieve') and resources ('comments authored by a user on issues', 'issue comments and notes'). It distinguishes from siblings like gitlab_get_issue (which gets issue details) or gitlab_get_user_activity_feed (which is broader).

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 with 'across all accessible projects and time periods' and lists specific use cases (track engagement, monitor patterns, etc.). However, it doesn't explicitly state when NOT to use this tool or name alternatives like gitlab_get_user_mr_comments for merge request comments.

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 effectively describes the tool's behavior: it's a read-only listing tool (implied by 'List'), returns paginated results (via per_page and page parameters), and includes details like commit info, MR context, and quality metrics. However, it doesn't explicitly mention rate limits, authentication needs, or error handling, leaving some 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 front-loaded with a clear purpose and usage guidelines, but it becomes verbose with repetitive sections like 'Use cases' and 'Returns merge-related commits with:' that overlap with earlier content. Sentences like 'Find all commits that originated from merge requests created by the specified user' are redundant, reducing efficiency.

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

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

Given no annotations, 6 parameters with 100% schema coverage, and no output schema, the description is mostly complete. It covers purpose, usage, parameters, and behavioral aspects like return details. However, it lacks explicit information on output structure (e.g., format of returned data) and error cases, which would enhance completeness for a tool with no output schema.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by explaining parameter semantics beyond the schema: it clarifies that 'user_id' and 'username' are alternatives ('use either'), provides context for 'target_branch' (e.g., 'main'), and explains the purpose of 'include_review_metrics' and pagination parameters. However, it lists parameters like 'user_id' not in the schema, causing minor inconsistency.

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: 'List merge commits where a specific user performed the merge.' It specifies the verb ('list'), resource ('merge commits'), and scope ('where a specific user performed the merge'), and distinguishes it from sibling tools by explicitly contrasting with 'gitlab_get_user_commits' for commits authored by the user.

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

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

The description provides explicit guidance on when to use this tool versus alternatives: 'For all commits authored by user, use 'gitlab_get_user_commits' instead.' It also includes use cases like 'release management' and 'merge activity tracking,' and clarifies the tool's specific focus on merges performed by the user, not authored by them.

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 describes what the tool returns (e.g., 'MR comment information' with details like content, type, MR context) and mentions pagination via 'per_page' and 'page' parameters. However, it lacks details on behavioral traits such as rate limits, authentication needs, error handling, or whether it's a read-only operation (though implied by 'Get'). This leaves gaps in transparency for an agent.

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 well-structured with clear sections: purpose, returns, use cases, parameters, and an example. It is appropriately sized and front-loaded with the core purpose. However, the 'Returns' section is somewhat verbose with bullet points that could be condensed, and the 'Use cases' might be redundant if the purpose is already clear, slightly reducing efficiency.

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

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

Given no annotations and no output schema, the description provides good context on what the tool does and its parameters. However, it lacks details on output format (only described in bullet points without schema), error conditions, or performance implications (e.g., pagination limits). For a tool with 6 parameters and complex filtering, this leaves some gaps in completeness for an agent to invoke it correctly.

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

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

Schema description coverage is 100%, so the schema already documents parameters well. The description adds value by listing all parameters with brief semantics (e.g., 'Filter by type (review, discussion, all)') and provides an example that clarifies usage. It compensates for the schema's lack of enums by explaining options like 'comment_type' and 'mr_state,' enhancing parameter understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Get all comments authored by a user on merge requests' with additional context about including 'code review discussions.' It specifies the resource (comments on merge requests) and action (get/find). However, it does not explicitly differentiate from sibling tools like 'gitlab_get_user_issue_comments' or 'gitlab_get_merge_request_notes,' which reduces clarity in distinguishing usage scenarios.

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 'Use cases' (e.g., 'Code review participation tracking') which imply when to use this tool, but it does not explicitly state when not to use it or name alternatives among sibling tools. For example, it doesn't clarify if this should be used instead of 'gitlab_get_merge_request_notes' for user-specific comments. This leaves usage context somewhat implied rather than explicit.

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 effectively describes key behaviors: the tool retrieves issues 'across all accessible projects' (scope), uses 'intelligent priority sorting' (sorting logic), returns a 'prioritized issue list' with detailed fields (output structure), and supports pagination via per_page and page parameters. It also mentions filtering capabilities and default values. While it doesn't cover rate limits or authentication needs, it provides substantial behavioral context beyond basic functionality.

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 well-structured with clear sections (purpose, usage, returns, use cases, parameters, example) and front-loads the core functionality. However, it includes some redundancy (e.g., listing parameters that are fully documented in the schema) and the 'Use cases' section, while helpful, adds length without critical new information. Most sentences earn their place, but minor trimming could improve efficiency.

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 (7 parameters, no output schema, no annotations), the description provides strong contextual completeness. It covers purpose, usage, behavioral traits, output structure, use cases, and parameters with an example. The main gap is the lack of an output schema, but the description compensates by detailing the return format ('Returns prioritized issue list with...'). It could be more explicit about error handling or permissions, but overall it's highly informative.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema: it clarifies that either user_id or username can be used ('use either user_id or username'), provides an example usage, and lists parameters with brief notes. However, it doesn't explain parameter interactions or add significant semantic context that isn't already in the schema descriptions, meeting the baseline for high schema coverage.

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

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

The description clearly states the specific action ('List open issues assigned to or created by a specific user') and resource ('issues'), distinguishing it from sibling tools like gitlab_get_issue (single issue) or gitlab_list_issues (general listing). It explicitly mentions the scope ('across all accessible projects') and purpose ('see what issues a user is currently working on or responsible for'), providing excellent 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 clear context for when to use this tool ('Use this tool to see what issues a user is currently working on or responsible for') and lists specific use cases (personal dashboard, workload management, SLA tracking, sprint planning). However, it doesn't explicitly state when NOT to use it or name alternative tools for similar purposes, such as gitlab_get_user_reported_issues or gitlab_get_user_resolved_issues, which could help avoid confusion.

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 discloses that the tool retrieves MRs 'across all accessible projects' and includes pagination behavior (via per_page and page parameters), which is useful context. However, it doesn't mention rate limits, authentication requirements, or error conditions that would be important for a read 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 well-structured with clear sections (purpose, returns, use cases, parameters, example). It's appropriately sized but could be more concise by eliminating redundant parameter listings since the schema already covers them thoroughly. Every sentence adds value, but some information is duplicated.

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 read-only tool with 5 parameters and no output schema, the description does a good job explaining what information is returned (MR details, status, urgency indicators, etc.) and provides use cases. However, without annotations or output schema, it could benefit from more behavioral context about limitations or error handling.

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

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

Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond the schema by listing parameters and providing an example, but doesn't explain semantics like the 'priority' sort option or the relationship between user_id and username beyond what's in the schema.

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

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

The description clearly states the tool's purpose: 'Get all open merge requests authored by a user' with specific verb ('Get'), resource ('open merge requests'), and scope ('authored by a user'). It distinguishes from siblings like gitlab_list_merge_requests (general listing) and gitlab_get_merge_request (single MR).

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

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

The description provides explicit use cases (personal MR dashboard, team workload monitoring, etc.) that guide when to use this tool. However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings (e.g., gitlab_get_user_review_requests for different MR contexts).

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 describes the return format in detail ('Returns reported issues with: - Issue details...'), which is helpful, but lacks information on permissions, rate limits, or error handling. The description does not contradict annotations, but it could be more comprehensive for a tool with 8 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 front-loaded with the core purpose but becomes verbose with repetitive sections like 'Use cases' and 'Parameters' that largely restate information. Sentences like 'Track personal issue reporting patterns' are somewhat redundant. While structured, it could be more streamlined by eliminating overlap and focusing on unique value-add.

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 (8 parameters, no output schema, no annotations), the description does a good job of covering purpose, usage, and return details. It explains what the tool returns in a structured way, which compensates for the lack of output schema. However, it could improve by addressing potential behavioral aspects like pagination handling or error scenarios more explicitly.

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

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

Schema description coverage is 100%, so the baseline is 3. The description lists all parameters and provides an example, but it does not add significant meaning beyond what the schema already documents (e.g., it repeats parameter names without extra context like validation rules or interdependencies). The example helps illustrate usage but doesn't enhance semantic understanding substantially.

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 ('List all issues created/reported by a specific user') and distinguishes it from sibling tools by explicitly mentioning 'gitlab_get_user_open_issues' as an alternative for assigned issues. It specifies the scope ('including closed ones', 'across all accessible projects') and clarifies the user's role ('original reporter/creator').

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

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

The description provides explicit guidance on when to use this tool vs. alternatives, stating 'For issues currently assigned to a user, use 'gitlab_get_user_open_issues' instead.' It also includes use cases and examples that help the agent understand appropriate contexts, such as 'Bug reporting patterns' and 'User feedback analysis,' making the usage boundaries clear.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns resolved issues with details like 'Issue details', 'Resolution info', and 'Timeline', which adds behavioral context beyond basic retrieval. However, it lacks information on permissions, rate limits, or error handling, leaving gaps for a tool with 8 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 well-structured with sections like 'Returns resolved issues with:', 'Use cases:', 'Parameters:', and an example, making it easy to scan. However, some details in the 'Returns' section (e.g., 'Recognition: contribution to project health') are somewhat verbose and could be trimmed for better 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 complexity (8 parameters, no annotations, no output schema), the description is moderately complete. It covers purpose, usage, and parameters but lacks details on output format, pagination behavior (beyond listing parameters), and error cases. This is adequate but has clear gaps for a tool with this level of complexity.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description lists parameters with brief notes (e.g., 'use either user_id or username'), adding minimal value beyond the schema. This meets the baseline of 3, as the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Get issues closed/resolved by a user') and resource ('issues'), distinguishing it from sibling tools like 'gitlab_get_user_open_issues' or 'gitlab_list_issues' by focusing on resolved issues rather than open ones or general listings. The title 'Get issues closed/resolved by a user' reinforces this specificity.

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 with 'Use cases' (e.g., 'Track problem resolution contributions', 'Performance reviews'), which implicitly guides when to use this tool. However, it does not explicitly state when not to use it or name alternatives (e.g., 'gitlab_get_user_open_issues' for unresolved issues), missing full explicit guidance.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns (resolved thread information with details like timeline, impact, etc.), which is helpful. However, it lacks critical behavioral information such as whether this is a read-only operation (implied but not stated), pagination behavior (mentioned in parameters but not explained in description), rate limits, authentication requirements, or error conditions. The description adds value but leaves significant 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 well-structured with sections for purpose, returns, use cases, parameters, and an example. Most sentences earn their place by adding value. However, it could be more front-loaded—the core purpose is clear early, but the detailed return list and use cases could be trimmed or integrated more tightly. The parameter list is somewhat redundant with the schema but serves as a quick reference.

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

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

Given no annotations, no output schema, and 6 parameters with full schema coverage, the description is moderately complete. It explains what the tool does and what it returns, but lacks behavioral details like pagination handling, error responses, or performance considerations. The use cases help contextualize, but for a tool with multiple parameters and no structured output, more guidance on result interpretation or limitations would improve completeness.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description lists parameters with brief explanations (e.g., 'user_id: Numeric user ID'), but these add minimal value beyond what's in the schema. It does clarify that 'username' and 'user_id' are alternatives ('use either user_id or username'), which is useful context not in the schema. However, parameters like 'resolution_type' and 'context_type' are listed without enum values or detailed semantics, so the description doesn't fully compensate for schema limitations.

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 threads resolved by a user in reviews') and resource ('discussion threads'), distinguishing it from siblings like gitlab_get_user_discussion_threads (which likely gets all threads) or gitlab_get_user_resolved_issues (which focuses on issues rather than threads). The verb 'Get' combined with the qualifier 'resolved by a user' makes the purpose unambiguous.

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

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

The description provides clear context for when to use this tool ('code review effectiveness tracking', 'collaboration quality assessment', etc.), but does not explicitly state when NOT to use it or name specific alternatives among the sibling tools. For example, it doesn't contrast with gitlab_get_user_discussion_threads or gitlab_get_user_resolved_issues, which could help the agent choose more precisely.

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 does well by describing what the tool returns (MR details, review status, priority indicators, action items, project context) and mentions pagination behavior. However, it doesn't disclose important behavioral aspects like authentication requirements, rate limits, error conditions, or whether this is a read-only operation (though 'Get' implies it).

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

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

The description is well-structured with clear sections (purpose, returns, use cases, parameters, example). However, the parameter section duplicates schema information unnecessarily, and the use cases section could be more concise. The front-loaded purpose statement is excellent, but later sections contain some 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 read operation with 6 parameters and no output schema, the description provides good context about what information is returned and how to use the tool. The absence of annotations means the description should ideally cover more behavioral aspects, but it does adequately explain the tool's purpose, usage, and expected results. The example helps clarify parameter 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?

With 100% schema description coverage, the schema already documents all 6 parameters thoroughly. The description repeats parameter information but adds minimal additional semantic context beyond what's in the schema. The example shows parameter usage but doesn't provide new insights about parameter interactions or constraints.

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 MRs where user is assigned as reviewer with pending action') and distinguishes it from sibling tools like 'gitlab_get_merge_request' or 'gitlab_get_user_open_mrs' by focusing specifically on pending review requests. The verb+resource combination is precise and unambiguous.

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

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

The description provides clear context about when to use this tool ('Personal review queue/inbox', 'Team code review management', etc.) and distinguishes it by focusing on pending review assignments. However, it doesn't explicitly state when NOT to use it or name specific alternative tools for different 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 the full burden of behavioral disclosure. It does well by describing the return format ('Returns snippet information with...') and pagination behavior (via per_page and page parameters), but doesn't mention rate limits, authentication requirements, or whether this is a read-only operation. The description implies it's a safe read operation but doesn't explicitly state this.

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 well-structured with clear sections (purpose, returns, use cases, parameters, example) and front-loads the core functionality. However, it's somewhat verbose with detailed bullet points in the returns section that could be more concise. The parameter section lists many parameters not in the actual schema, creating some 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 tool with 3 parameters, 100% schema coverage, and no output schema, the description provides good context about return values and use cases. However, there's a significant mismatch between the 8 parameters described and the 3 parameters in the actual schema, creating confusion. The description compensates for the lack of output schema by detailing return information, but the parameter discrepancy reduces overall completeness.

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

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

The schema description coverage is 100%, so the schema already documents all 3 parameters. The description adds significant value by listing 8 parameters with semantic explanations (scope, visibility, language, sort, etc.) that aren't in the actual schema. This provides crucial context about filtering capabilities beyond the minimal required parameters, though there's a disconnect between described parameters and actual 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 tool's purpose with specific verb ('List all personal and project snippets created by a user') and resource ('code snippets'), and distinguishes it from sibling tools like 'gitlab_get_snippet' (singular) and 'gitlab_list_snippets' (general listing). The title 'List all...' establishes this as a filtered listing operation focused on user-created snippets.

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 ('Find all code snippets created by the specified user across personal and project scopes') and includes use cases like 'Personal code library management' and 'Code reuse and template management.' However, it doesn't explicitly state when NOT to use it or mention alternatives like 'gitlab_list_snippets' for general snippet browsing or 'gitlab_get_snippet' for single snippet details.

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 return format ('All branches with latest commit info') and provides an example response, which is helpful. However, it doesn't mention important behavioral aspects like whether this is a paginated endpoint, rate limits, authentication requirements, or error conditions.

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 well-structured with clear sections (purpose, returns, usage, optional feature, example, related tools). While the example response is detailed, it serves a useful purpose in clarifying the output format. The related tools section is helpful but could be more concise by focusing only on the most relevant siblings.

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 and no output schema, the description provides reasonable coverage of purpose and usage. The example response partially compensates for the lack of output schema. However, important contextual information about authentication, pagination, error handling, and the relationship to other list tools in the extensive sibling set is missing.

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

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

Schema description coverage is 100% with one parameter documented in the schema. The description mentions 'Optional: Search filter' which doesn't directly correspond to the 'project_id' parameter in the schema, creating some confusion. It adds minimal value beyond what the schema already provides about the single parameter.

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

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

The description clearly states the verb ('List') and resource ('repository branches'), making the purpose immediately understandable. It distinguishes from some siblings like 'gitlab_create_branch' and 'gitlab_delete_branch' by indicating a read operation, though it doesn't explicitly differentiate from other list tools like 'gitlab_list_tags' or 'gitlab_list_commits' in the same repository 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 'Use when' section provides clear context ('Checking branches, finding feature branches'), which helps the agent understand appropriate scenarios. It mentions an optional search filter, though it doesn't explicitly state when NOT to use this tool or name specific alternatives among the many 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 and does well by disclosing key behavioral traits: it mentions pagination ('Yes (default 20 per page)'), filtering capabilities ('By date range, file path, branch'), and provides an example response structure. However, it doesn't cover aspects like rate limits or authentication needs, which could be relevant for a read 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 well-structured and front-loaded with key information (purpose, returns, usage, filtering, pagination), followed by an example and related tools. Every sentence adds value without redundancy, making it efficient and easy to scan.

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 output schema, no annotations), the description is quite complete: it covers purpose, usage, filtering, pagination, example response, and related tools. However, it could be more comprehensive by explicitly mentioning that it's a read-only operation or any error conditions, though the lack of annotations and output schema is partially mitigated by the detailed schema.

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