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lawp09

bitbucket-mcp

by lawp09

Suggest Pull Request Reviewers

suggest_pull_request_reviewers
Read-onlyIdempotent

Suggests relevant reviewers for a pull request by combining default reviewers with frequent approvers from recently merged PRs. Returns scored suggestions with reasons.

Instructions

Suggest reviewers for a pull request based on default reviewers and recent PR history.

Combines default reviewers with frequent approvers from recently merged PRs to suggest the most relevant reviewers.

Args: repo_slug: Repository slug pull_request_id: Pull request ID max_suggestions: Maximum number of reviewer suggestions (default: 5) workspace: Workspace name (optional, defaults to configured workspace)

Returns: Suggested reviewers with scores and reasons, already-assigned reviewers, and data sources

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_slugYes
workspaceNo
max_suggestionsNo
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 discloses the method (combining default reviewers and recent PR history) and what it returns (scores, reasons, already-assigned reviewers, data sources). Annotations already indicate read-only and idempotent, so no contradiction.

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

Conciseness3/5

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

The description is structured with a summary paragraph and a list, but the Args section largely repeats schema information, making it slightly longer than necessary.

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

Completeness4/5

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

Given the presence of annotations and output schema, the description adds sufficient context about the tool's behavior and return values, though it could specify sorting or limit behavior more explicitly.

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

Parameters4/5

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

The Args section explains each parameter's purpose beyond the schema, including defaults and optionality, compensating for the 0% schema description coverage.

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 'suggest' and the resource 'pull request reviewers', and it distinguishes from sibling tools as no other tool suggests reviewers.

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 explains that it combines default reviewers with frequent approvers, providing context for when to use it, but it lacks explicit guidance on when not to use it or alternatives.

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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