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

No annotations are provided, so the description carries full burden. While '接受并合并' implies a write operation that modifies the repository, it doesn't disclose critical behavioral traits: whether this requires specific permissions (e.g., maintainer role), if it's irreversible, potential side effects (e.g., branch deletion), rate limits, or what happens on failure. The mention of '自定义合并选项' (custom merge options) hints at configurability but lacks specifics.

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

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

The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more informative without sacrificing 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 (a write operation with 4 parameters, nested objects, and no output schema) and lack of annotations, the description is incomplete. It doesn't explain return values, error conditions, or important behavioral context (e.g., merge strategies, permissions). For a tool that performs a significant repository action, this leaves critical gaps for an AI agent.

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

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

Schema description coverage is 100%, so the schema already documents all 4 parameters. The description adds minimal value beyond the schema by mentioning '自定义合并选项' (custom merge options), which aligns with the 'mergeOptions' parameter but doesn't elaborate on semantics. With high schema coverage, the baseline is 3.

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

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

The description clearly states the action ('接受并合并' - accept and merge) and the resource ('指定项目的合并请求' - specified project's merge request). It distinguishes from sibling tools like 'Gitlab Create MR Tool' and 'Gitlab Update MR Tool' by focusing on finalizing merge requests. However, it doesn't explicitly mention that this is for GitLab specifically (though the tool name implies it).

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., merge request must be in a mergeable state), when not to use it (e.g., for draft MRs), or how it differs from sibling tools like 'Gitlab Update MR Tool' which might handle MR modifications without 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 full burden for behavioral disclosure. It states the action ('添加评论') which implies a write operation, but doesn't describe permissions required, rate limits, whether comments are editable/deletable, or what happens on success/failure. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.

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

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

The description is a single, efficient sentence in Chinese that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core action, making it easy to parse quickly.

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

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

For a mutation tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like permissions or error handling, nor does it explain the purpose of the optional 'fields' parameter or what the tool returns. More context is needed given the complexity and lack of structured data.

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 4 parameters (projectId, mergeRequestId, comment, fields). The description doesn't add any parameter-specific context beyond what's in the schema, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the action ('添加评论' - add comment) and resource ('指定项目的合并请求' - specified project's merge request). It distinguishes from siblings like 'Gitlab Accept MR Tool' or 'Gitlab Update MR Tool' by focusing on commenting rather than merging or modifying the MR itself. However, it doesn't explicitly differentiate from potential commenting alternatives that might exist in other contexts.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing MR), exclusions, or comparisons with sibling tools like 'Gitlab Raw API Tool' which might also handle comments. Usage is implied through the action but not explicitly contextualized.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While '创建新的 Merge Request' implies a write/mutation operation, the description lacks critical details: it doesn't mention authentication requirements, potential side effects (e.g., branch merges), error conditions, or what the tool returns. For a creation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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

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

The description is extremely concise—a single sentence in Chinese that directly states the tool's core function and key features. It's front-loaded with the primary action and wastes no words, making it easy to parse quickly. Every part of the sentence earns its place by highlighting essential capabilities.

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 creating a Merge Request (a mutation with 9 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain the return value, error handling, or important behavioral aspects like required permissions or idempotency. While the schema covers parameters well, the overall context for safe and effective use is lacking.

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 9 parameters thoroughly. The description adds minimal value beyond the schema by mentioning assignee and reviewer support, which aligns with the 'assigneeId' and 'reviewerIds' parameters but doesn't provide additional context like format examples or 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 tool's purpose: '创建新的 Merge Request' (create a new Merge Request) with specific capabilities: '支持指派 assignee 和 reviewers' (supports assigning assignee and reviewers). This is a specific verb+resource combination that distinguishes it from siblings like Gitlab Update MR Tool or Gitlab Accept MR Tool. However, it doesn't explicitly differentiate from Gitlab Create MR Comment Tool beyond the core action.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing existing branches), compare it to Gitlab Update MR Tool for modifications, or specify scenarios where it's appropriate. The agent must infer usage from the name and parameters alone, which is insufficient for clear decision-making.

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. While it mentions filtering capabilities, it fails to describe critical behavioral aspects such as whether this is a read-only operation (implied but not stated), authentication requirements, rate limits, pagination behavior, or error handling. For a tool with zero annotation coverage, this represents a significant gap in transparency.

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

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

The description is extremely concise - a single sentence that efficiently communicates the core purpose and key capability. Every word earns its place with no wasted verbiage, 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 tool with 2 parameters, no annotations, and no output schema, the description is insufficiently complete. While concise, it doesn't compensate for the lack of structured metadata by explaining what the tool returns, how results are formatted, or important behavioral constraints. The agent would need to guess about the output format and operational characteristics.

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

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

The description mentions '支持多种过滤条件' (supports multiple filtering conditions), which aligns with the two parameters in the schema. However, with 100% schema description coverage (both parameters are well-documented in the schema), the description adds minimal value beyond what's already in the structured data. It doesn't provide additional context about parameter interactions or usage patterns.

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 ('获取' - get/retrieve) and resource ('当前用户的待办任务' - current user's pending tasks), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'Gitlab Search Project Details Tool' or 'Gitlab Search User Projects Tool' that might also retrieve user-related data, which prevents a perfect score.

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

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

The description mentions '支持多种过滤条件' (supports multiple filtering conditions), which implies some context for usage, but provides no explicit guidance on when to use this tool versus alternatives like 'Gitlab Search User Projects Tool' or 'Gitlab Raw API Tool'. There are no when-to-use or when-not-to-use statements, leaving the agent to infer usage scenarios.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only mentions the tool is for '调试和高级用法' (debugging and advanced usage), which hints at flexibility but doesn't disclose critical behavioral traits like authentication requirements, rate limits, error handling, or what happens with destructive operations (DELETE/PUT/PATCH methods). For a generic API tool with 5 parameters including destructive HTTP methods, this is a significant gap.

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

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

The description is extremely concise with just one sentence in Chinese: '支持自定义调用任意 GitLab REST API，适合调试和高级用法。' (Supports custom calls to any GitLab REST API, suitable for debugging and advanced usage). Every word earns its place - it states the core capability and primary use cases without any fluff or repetition.

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

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

Given this is a complex, generic API tool with 5 parameters (including destructive HTTP methods), no annotations, and no output schema, the description is insufficiently complete. It doesn't address authentication, error responses, rate limits, or provide guidance on constructing valid API calls. For a tool that essentially provides raw HTTP access to GitLab's API, users need more context about safe usage patterns and limitations.

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

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

The description adds no parameter-specific information beyond what's already in the schema (which has 100% coverage). It doesn't explain how parameters interact, provide examples beyond what's in schema descriptions, or clarify edge cases. With high schema coverage, the baseline is 3, but the description doesn't compensate with additional semantic context about parameter usage patterns.

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: '支持自定义调用任意 GitLab REST API' (supports custom calls to any GitLab REST API). It specifies the verb ('调用' - call/invoke) and resource ('GitLab REST API'), making it clear this is a generic API caller. However, it doesn't explicitly distinguish itself from sibling tools like 'Gitlab Search Project Details Tool' or 'Gitlab Update MR Tool' which are specific API endpoints.

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: '适合调试和高级用法' (suitable for debugging and advanced usage). This implies it should be used when specific sibling tools don't cover needed functionality or for custom API exploration. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions field filtering and efficiency improvements but lacks critical information: authentication requirements, rate limits, pagination behavior, error conditions, or what constitutes 'detailed information'. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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

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

The description is a single, efficient Chinese sentence that communicates the core functionality. It's appropriately sized without unnecessary elaboration. While front-loaded with the main purpose, it could potentially benefit from separating efficiency claims into a second sentence for better structure.

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

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

For a search tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'detailed information' includes, how results are formatted, whether authentication is required, or how errors are handled. The agent lacks necessary context to properly understand the tool's behavior and output.

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

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

The description mentions field filtering ('支持字段过滤') which aligns with the 'fields' parameter in the schema. However, with 100% schema description coverage, the input schema already fully documents both parameters with examples. The description adds minimal value beyond what's already in the structured schema, meeting the baseline for high schema coverage.

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

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

The description clearly states the tool searches for projects by name and returns detailed information with field filtering. It specifies the verb ('搜索' - search), resource ('项目' - projects), and scope ('详细信息' - detailed information). However, it doesn't explicitly differentiate from sibling tools like 'Gitlab Search User Projects Tool' which might have overlapping functionality.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'Gitlab Search User Projects Tool' or 'Gitlab Raw API Tool'. It mentions efficiency benefits ('提升响应效率') but doesn't specify use cases, prerequisites, or exclusions. The agent receives no direction on tool selection.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions field filtering for efficiency but doesn't cover critical aspects: whether this is a read-only operation (implied by '搜索' but not explicit), authentication requirements, rate limits, error handling, or what '活跃项目' (active projects) means. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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

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

The description is concise with two sentences: one stating the purpose and one about field filtering. It's front-loaded with the main function. However, the second sentence ('支持字段过滤，提升响应效率') could be integrated more smoothly, and there's room to add usage context without losing 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 complexity (search tool with user and project data), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what '用户信息及其活跃项目' includes (e.g., specific fields or project criteria), how results are structured, or any limitations. For a tool that likely returns rich data, this leaves the agent with insufficient context to use it effectively.

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

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

The schema description coverage is 100%, so the schema already documents both parameters ('username' and 'fields') thoroughly. The description adds minimal value beyond the schema: it reiterates field filtering ('支持字段过滤') but doesn't provide additional context like examples beyond the schema's or explain how '活跃项目' relates to parameters. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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

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

The description clearly states the tool's purpose: '根据用户名搜索用户信息及其活跃项目' (search for user information and their active projects by username). It specifies the verb ('搜索' - search) and resource ('用户信息及其活跃项目' - user information and active projects). However, it doesn't explicitly differentiate from sibling tools like 'Gitlab Search Project Details Tool' or 'Gitlab Get User Tasks Tool', which prevents a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions '支持字段过滤，提升响应效率' (supports field filtering to improve response efficiency), which is a feature but not usage context. There's no mention of when to choose this over 'Gitlab Get User Tasks Tool' or 'Gitlab Search Project Details Tool', nor any prerequisites or exclusions.

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

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

No annotations are provided, so the description carries full burden. It states this is an update operation, implying mutation, but doesn't disclose behavioral traits such as required permissions, whether changes are reversible, rate limits, or what the response looks like (e.g., success/failure indicators). For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core purpose. It avoids unnecessary words and directly states the tool's function. However, it could be slightly more structured by explicitly listing all updatable fields for 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?

Given the complexity (mutation tool with 8 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like error handling, permissions, or response format, and provides minimal usage guidance. This is inadequate for a tool that modifies resources in a collaborative system like GitLab.

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 8 parameters thoroughly. The description adds minimal value by mentioning 'assignee 和 reviewers', which maps to 'assigneeId' and 'reviewerIds' parameters, but doesn't provide additional context beyond what's in the schema. This meets the baseline of 3 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 action ('更新' meaning update) and resource ('指定项目的 Merge Request'), and specifies what can be updated ('包括指派 assignee 和 reviewers'). However, it doesn't explicitly differentiate from sibling tools like 'Gitlab Create MR Tool' or 'Gitlab Accept MR Tool', which would require more specific scope or condition statements.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing MR), exclusions, or compare to siblings like 'Gitlab Create MR Tool' for creation or 'Gitlab Accept MR Tool' for merging. This leaves the agent without context for tool selection.

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