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SharkyND

mcp-atlassian

by SharkyND
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

bitbucket_analyze_pr_review_status

Analyze pull request comments to identify which review feedback is addressed and which remains pending, providing a structured summary of addressed and pending comments along with overall status.

Instructions

Analyze a pull request's comment threads to determine which review feedback has been addressed and which is still pending.

For each comment thread the tool inspects:

  • Whether the comment is explicitly resolved/marked done (Server/DC state field, Cloud resolved flag).

  • Replies in the thread — if a reply contains completion keywords ("done", "fixed", "addressed", "resolved", "updated", "completed") it is considered addressed even when no formal resolve action was taken.

Returns a structured summary with:

  • total_comments: number of top-level review comments found

  • addressed: list of comments considered done (resolved or positively replied)

  • pending: list of comments that still need attention

  • overall_status: "ALL_ADDRESSED" | "PARTIALLY_ADDRESSED" | "NONE_ADDRESSED"

Args: workspace: Workspace name or project key. repository: Repository name. pull_request_id: Pull request ID.

Returns: JSON string with the review status analysis.

Raises: ValueError: If the Bitbucket client is not configured or available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceYesWorkspace name (Cloud) or project key (Server/DC)
repositoryYesRepository name
pull_request_idYesPull request ID to analyze

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.1/5.0
Behavior4/5

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

With no annotations, the description fully details the analysis logic (resolved status, reply keywords), output fields, and error condition. Could be improved by stating it's read-only or required permissions.

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, well-structured with a clear first sentence, bullet points for logic, then return summary, args, and raises. Every sentence adds value.

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?

Given an output schema exists, the description still adequately covers input, logic, output fields, and errors, making it fully complete for the tool's complexity.

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?

Schema coverage is 100%, so the description adds minimal extra over schema descriptions. It repeats parameter names and provides a brief context for workspace but not significant new info.

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 verb 'analyze' and the resource 'pull request comment threads' to determine addressed vs pending feedback. It distinguishes from sibling tools like adding comments or fetching PR diffs.

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 implicitly indicates when to use (to review comment status) but lacks explicit guidance on when not to use or comparisons to alternatives like bitbucket_get_pull_request.

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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