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yjiace

AlibabaCloud DevOps MCP Server

by yjiace

list_change_request_comments

Retrieve comments on change requests in Alibaba Cloud DevOps to track feedback and discussions during code review processes.

Instructions

[Code Management] List comments on a change request

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
organizationIdYesOrganization ID, can be found in the basic information page of the organization admin console
repositoryIdYesRepository ID or a combination of organization ID and repository name, for example: 2835387 or organizationId%2Frepo-name (Note: slashes need to be URL encoded as %2F)
localIdYesChange request local ID
patchSetBizIdsNoAssociated version ID list, each comment is associated with a version, indicating which version the comment was posted on, for global comments, it's associated with the latest merge source version
commentTypeNoComment type. Possible values: GLOBAL_COMMENT, INLINE_COMMENTGLOBAL_COMMENT
stateNoComment state. Possible values: OPENED, DRAFTOPENED
resolvedNoWhether marked as resolved
filePathNoFilter by file path (for inline comments)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but only states the basic action. It does not cover critical aspects like whether this is a read-only operation, pagination behavior, rate limits, authentication needs, or what the output looks like, which are essential 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.

Conciseness5/5

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

The description is extremely concise with a single sentence that front-loads the core purpose efficiently. There is no wasted verbiage, and every word earns its place, making it structurally optimal for quick comprehension.

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

Completeness2/5

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 insufficiently complete. It lacks details on behavioral traits, output format, error handling, or usage context, leaving significant gaps for an agent to understand how to effectively invoke this tool.

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

Parameters3/5

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 documented in the schema itself. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 without compensating or detracting.

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

Purpose4/5

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 ('comments on a change request') with a domain context ('[Code Management]'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'list_work_item_comments' or 'create_change_request_comment', which would be needed for a score of 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, such as filtering scenarios or prerequisites. It lacks any mention of when-not-to-use or sibling tool comparisons, leaving usage context entirely implicit.

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

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