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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Create' implies a write operation, it doesn't specify required permissions, whether the action is reversible, rate limits, or what happens on success/failure. 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 states the core purpose without unnecessary words. It's appropriately sized and front-loaded, with every word earning its place in conveying the essential action.

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

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

For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address behavioral aspects like permissions, side effects, or response format, nor does it differentiate from sibling tools. Given the complexity of creating review comments in a collaborative system, more context is needed.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema (e.g., it doesn't explain format expectations for 'body' or constraints on 'merge_request_iid'). 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 ('Create a new discussion thread') and target resource ('in a merge request review'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'reply_to_review_comment' or 'resolve_review_discussion', which prevents a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'reply_to_review_comment' (for responding to existing discussions) or 'get_merge_request_reviews' (for reading reviews). There's no mention of prerequisites, context, or exclusions, leaving usage entirely implicit.

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 'Get all merge requests' implies a read operation, it doesn't specify whether this returns all historical merge requests or only active ones, whether results are paginated, what permissions are required, or what format the output takes. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's appropriately sized for a simple retrieval tool and front-loads the essential information.

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

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

Given the lack of annotations and output schema, the description is incomplete for a tool that presumably returns multiple merge requests. It doesn't address key contextual aspects like response format, pagination, filtering options, or error conditions. For a retrieval tool with no structured output documentation, more completeness is needed.

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

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

The schema description coverage is 100%, with the single parameter 'branch_name' fully documented in the schema. The description doesn't add any additional semantic context about the parameter beyond what the schema already provides, such as branch naming conventions or examples. With complete schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the action ('Get all merge requests') and target resource ('for a specific branch'), providing a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from sibling tools like 'list_merge_requests' or 'get_merge_request_details', which would require sibling differentiation for 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. With sibling tools like 'list_merge_requests' and 'get_merge_request_details' available, there's no indication of when this branch-specific retrieval is preferred over other merge request-related operations.

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 what the tool does but doesn't cover key traits like whether it's read-only, paginated, rate-limited, or requires specific permissions. For a tool fetching discussions, this lack of context is a significant gap.

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

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

The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It's appropriately sized for a simple tool and earns its place by clearly stating the action and scope.

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

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

Given the complexity of fetching discussions (which may involve nested data), no annotations, and no output schema, the description is incomplete. It doesn't explain what 'discussions and comments' entail, the return format, or any behavioral aspects, leaving gaps for effective tool use.

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

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

The schema has 100% description coverage, with the parameter 'merge_request_iid' well-documented in the schema. The description adds no additional meaning beyond implying the parameter's role, 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 action ('Get discussions and comments') and the target resource ('on commits within a specific merge request'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_merge_request_reviews' or 'reply_to_review_comment', which might also involve discussions, so it misses full sibling distinction.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing an existing merge request, or compare it to siblings like 'get_merge_request_reviews' for broader review contexts. Usage is implied by the parameter but not explicitly stated.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool is 'perfect for debugging' but fails to specify whether this is a read-only operation, if it requires specific permissions, rate limits, or what the output format looks like (e.g., raw text, structured logs). This leaves significant gaps for an agent to understand 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?

The description is front-loaded with the core purpose in the first sentence, followed by a practical use case. Both sentences earn their place by adding value—no wasted words or redundancy. It's efficiently structured for quick comprehension.

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

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

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is adequate but incomplete. It explains the purpose and usage context well but lacks details on behavioral aspects like permissions or output format, which are crucial for a debugging tool. This results in a minimal viable description with clear gaps.

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

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

The schema description coverage is 100%, with the job_id parameter fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. However, with only one parameter and high schema coverage, the baseline of 3 is appropriate.

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

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

The description clearly states the specific action ('Get the trace/log output') and target resource ('for a specific pipeline job'), distinguishing it from siblings like get_merge_request_test_report or get_pipeline_test_summary. It provides concrete use cases ('debugging failed tests and understanding CI/CD failures'), making the purpose immediately understandable.

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 implicitly suggests usage for debugging and understanding failures, which provides clear context for when to use this tool. However, it lacks explicit guidance on when not to use it or direct alternatives among siblings (e.g., get_pipeline_test_summary for aggregated 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 of behavioral disclosure. It states this is a read operation ('Get'), but doesn't cover critical aspects like authentication requirements, rate limits, error conditions, or what 'detailed information' includes (e.g., fields returned, format). 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 a single, focused sentence that efficiently conveys the core purpose without unnecessary words. It's appropriately sized for a simple retrieval tool and front-loads the essential information ('Get detailed information'). Every word earns its place, making it highly concise.

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 (single parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks context about behavioral traits, usage guidelines, and output expectations. For a tool with no annotations or output schema, the description should do more to compensate, but it meets the bare minimum for viability.

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 the single parameter 'merge_request_iid' clearly documented in the schema as 'Internal ID of the merge request'. The description adds no additional parameter semantics beyond what the schema provides, such as where to find this ID or format examples. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the verb ('Get') and resource ('detailed information about a specific merge request'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_merge_requests' or 'get_merge_request_reviews', which would require more specific language about what 'detailed information' entails compared to those alternatives.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a specific merge request ID), contrast with 'list_merge_requests' for bulk retrieval, or specify use cases where detailed vs. summary information is needed. This leaves the agent to infer usage from the tool name alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return data (pipeline data with jobs and statuses) and mentions job IDs for use with get_job_log, adding useful context. However, it lacks details on permissions, rate limits, or error handling, which are important for a tool with no annotations.

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

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

The description is front-loaded with the core purpose in the first sentence and adds a helpful second sentence about using job IDs with get_job_log. Both sentences earn their place by providing essential information without redundancy or fluff.

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 (1 parameter, no output schema, no annotations), the description is mostly complete. It explains what the tool returns and how to use the output with another tool. However, it could improve by addressing behavioral aspects like error cases or data freshness, which are not covered by annotations or 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 input schema has 100% description coverage, fully documenting the merge_request_iid parameter. The description does not add any parameter-specific information beyond what the schema provides, 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 specific action ('Get the last pipeline data') and resource ('for a specific merge request'), with details on what data is included ('including all jobs and their statuses'). It distinguishes from siblings like get_merge_request_details or get_pipeline_test_summary by focusing on pipeline data rather than general MR info or test summaries.

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—to retrieve pipeline data for a merge request—and hints at usage with get_job_log for detailed output. However, it does not explicitly state when not to use it or name alternatives among siblings, such as get_merge_request_test_report for test-related 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 of behavioral disclosure. It states what the tool does but doesn't describe how it behaves—such as whether it returns all reviews/discussions, supports pagination, requires specific permissions, or has rate limits. This leaves significant 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 a single, clear sentence that efficiently conveys the core purpose without unnecessary words. It's front-loaded and appropriately sized for the tool's complexity, with no wasted 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 lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what 'reviews and discussions' entail, the return format, or behavioral aspects like error handling. For a tool with no structured metadata, more contextual detail is needed.

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

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

The schema description coverage is 100%, with the single parameter 'merge_request_iid' well-documented in the schema. The description doesn't add any additional meaning or context about the parameter beyond what the schema provides, so it 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 ('Get') and target resources ('reviews and discussions for a specific merge request'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_merge_request_details' or 'get_commit_discussions', which might also retrieve related information.

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, appropriate contexts, or comparisons to sibling tools like 'get_merge_request_details' or 'get_commit_discussions', 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 the full burden of behavioral disclosure. It mentions that the report shows 'the same test data visible on the GitLab MR page,' which adds useful context about data consistency. However, it lacks details on permissions, rate limits, or response format, leaving gaps for a tool that fetches potentially sensitive test data.

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

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

The description is front-loaded and concise, consisting of two sentences that efficiently convey purpose and usage. Every sentence adds value without redundancy, making it easy for an agent to parse and understand quickly.

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

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

Given the tool's complexity (fetching detailed test reports) and lack of annotations or output schema, the description is adequate but incomplete. It covers the purpose and use case well but omits behavioral details like error handling or data format, which could hinder an agent's ability to invoke it correctly without additional context.

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

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

The input schema has 100% description coverage, with the parameter merge_request_iid well-documented. The description does not add any additional meaning or context beyond what the schema provides, such as explaining how to find the IID or its relationship to other identifiers. 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 a specific verb ('Get'), resource ('structured test report for a merge request'), and scope ('with specific test failures, error messages, and stack traces'). It distinguishes from siblings like get_merge_request_details or get_pipeline_test_summary by focusing on detailed test failure data for debugging.

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 ('Best for debugging test failures'), which implicitly differentiates it from siblings that might provide summaries or other MR data. However, it does not explicitly state when not to use it or name specific alternatives, such as get_pipeline_test_summary for high-level overviews.

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 communicates that this is a read operation (implied by 'Get'), provides performance characteristics ('faster', 'lightweight'), and indicates the output format ('pass/fail counts per test suite'). However, it doesn't mention potential limitations like 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 perfectly concise with three tightly focused sentences that each serve a distinct purpose: stating the tool's function, comparing it to alternatives, and providing usage guidance. There's no wasted language or redundancy.

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

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

For a single-parameter read tool with no output schema, the description provides excellent context about what the tool returns ('pass/fail counts per test suite') and how it differs from alternatives. The main gap is the lack of output format details, but given the simplicity of the tool and clear behavioral description, it's mostly 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 the single parameter 'merge_request_iid' well-documented in the schema. The description doesn't add any parameter-specific information beyond what the schema already provides, so it meets the baseline of 3 for high schema coverage without adding extra 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 specific action ('Get test summary'), target resource ('for a merge request'), and scope ('lightweight overview showing pass/fail counts per test suite'). It distinguishes from the sibling tool 'get_merge_request_test_report' by emphasizing it's 'faster than full test report' and 'great for quick status checks'.

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 when to use this tool ('Great for quick status checks') and when to use an alternative ('Faster than full test report'), clearly distinguishing it from the sibling 'get_merge_request_test_report'. This gives the agent clear guidance on selecting between these two test-related tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it's a list operation but doesn't mention whether it's read-only, paginated, rate-limited, requires authentication, or what the output format looks like. This is a significant gap for a tool with no annotation coverage.

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

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

The description is a single, efficient sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like pagination, authentication needs, or return format, which are crucial for a list operation. The schema covers parameters well, but overall context is insufficient.

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 all parameters are documented in the schema. The description adds no additional meaning about parameters beyond what the schema provides (e.g., it doesn't explain filtering logic or result ordering). 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 ('List') and resource ('merge requests for the GitLab project'), making the purpose immediately understandable. It doesn't explicitly distinguish from siblings like 'get_branch_merge_requests' or 'get_merge_request_details', which would require a 5.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_branch_merge_requests' (which might filter by branch) or 'get_merge_request_details' (which retrieves a specific MR). It lacks any context about prerequisites, typical use cases, or exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Reply to') but lacks details on permissions required, whether replies are editable/deletable, rate limits, or response format. This is a significant gap for a mutation tool with zero annotation coverage.

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

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

The description is a single, clear sentence that efficiently conveys the core purpose without unnecessary words. It is appropriately sized and front-loaded, with every word earning 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 complexity (a mutation with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It does not address behavioral aspects like permissions or response format, which are crucial for an agent to use this tool effectively in context.

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

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

The input schema has 100% description coverage, so parameters are well-documented in the schema. The description does not add any additional meaning or context beyond what the schema provides, such as explaining how to obtain 'discussion_id' or formatting for 'body'. 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 ('Reply to') and target ('a specific discussion thread in a merge request review'), making the purpose understandable. However, it does not explicitly differentiate from sibling tools like 'create_review_comment' or 'resolve_review_discussion', which might involve similar actions on review comments or discussions.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., needing an existing discussion thread), exclusions, or comparisons to siblings like 'create_review_comment' (for new comments) or 'resolve_review_discussion' (for closing 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 the full burden of behavioral disclosure. It states the action but lacks critical details: whether this requires specific permissions, if it's reversible (beyond the 'resolved' parameter), what happens to the discussion thread after resolution, or any rate limits/constraints. 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 action and target. Every word earns its place with no redundancy or fluff, making it easy to parse quickly while conveying essential information.

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

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

Given the tool's complexity (a mutation with no annotations and no output schema), the description is incomplete. It doesn't address behavioral aspects like permissions, side effects, or response format, nor does it guide usage relative to siblings. For a tool that modifies state, more context is needed to ensure safe and correct invocation.

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

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

Schema description coverage is 100%, so the schema fully documents all three parameters. The description adds no additional meaning beyond what's in the schema—it doesn't explain parameter relationships, format specifics, or edge cases. This meets the baseline for high schema coverage but doesn't enhance understanding.

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

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

The description clearly states the action ('resolve or unresolve') and target ('a discussion thread in a merge request review'), making the purpose immediately understandable. It specifies the resource type (discussion thread) and context (merge request review), though it doesn't explicitly differentiate from sibling tools like 'reply_to_review_comment' or 'create_review_comment' that also interact with discussions.

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 discussion thread), exclusions, or relationships to sibling tools like 'get_merge_request_reviews' or 'get_commit_discussions' that might be used to find discussions first. Usage is implied but not explicitly stated.

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