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lawp09

bitbucket-mcp

by lawp09

Submit Batch Review

submit_pull_request_batch_review

Submit a batch review on a pull request by posting multiple inline or general comments, then approve, request changes, or comment only.

Instructions

Submit a batch review on a pull request: post multiple comments and optionally approve or request changes.

Note: Bitbucket API does not support pending/draft comments in batch. Each comment is posted immediately.

Args: repo_slug: Repository slug pull_request_id: Pull request ID comments: List of comment objects. Each comment has: - content (str, required): Comment text in markdown - inline (dict, optional): For inline comments: {"path": str, "to": int, "from": int} review_action: Action after posting comments: "approve", "request_changes", or "comment_only" (default: "comment_only") review_message: General review message posted as a top-level comment (optional) workspace: Workspace name (optional, defaults to configured workspace)

Returns: Summary with comments_posted count, action taken, and PR details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commentsYes
repo_slugYes
workspaceNo
review_actionNocomment_only
review_messageNo
pull_request_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description adds behavioral context by noting that Bitbucket API does not support pending/draft comments in batch and that each comment is posted immediately. This goes beyond the annotations, which lack any behavioral hints.

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 concise and well-structured, with a clear purpose statement, a behavioral note, and a neatly formatted parameter list. Every sentence adds value without redundancy.

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

Completeness5/5

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

The description covers all parameters, returns a summary, and includes a behavioral note. With an output schema present, it does not need to detail return values, but it still provides a concise overview, making the tool fully understandable.

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

Parameters5/5

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

With 0% schema coverage, the description fully explains the parameters: comments structure (content, inline), review_action enum values, and optional fields. This adds essential meaning that the schema alone does not provide.

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

Purpose5/5

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

The description clearly states the tool's purpose: submitting a batch review on a pull request with multiple comments and optional approval/request changes. This differentiates it from sibling tools like add_pull_request_comment (single comment) or approve_pull_request (only approval).

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

Usage Guidelines3/5

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

The description implies usage for batch operations but does not explicitly state when to use this tool over alternatives like multiple single-comment calls. Guidance is implicit rather than explicit.

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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