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saidsef

GitHub PR Issue Analyser

by saidsef

update_reviews

Submit reviews for GitHub pull requests to approve changes, request modifications, or add comments. This tool automates code review feedback within the GitHub PR Issue Analyser server.

Instructions

Submits a review for a specific pull request in a GitHub repository. Args: repo_owner (str): The owner of the repository. repo_name (str): The name of the repository. pr_number (int): The pull request number to review. event (Literal['APPROVE', 'REQUEST_CHANGES', 'COMMENT']): The type of review event. body (str, optional): Required when using REQUEST_CHANGES or COMMENT for the event parameter. Defaults to None. Returns: Dict[str, Any]: The JSON response from the GitHub API containing review information if successful. None: If an error occurs during the review submission process. Error Handling: Logs errors and prints the traceback if the review submission fails, returning None.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_ownerYes
repo_nameYes
pr_numberYes
eventYes
bodyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function that implements the core logic of the 'update_reviews' MCP tool. It sends a POST request to GitHub's pull request reviews API endpoint to submit a review with the specified event type and optional body comment.
    def update_reviews(self, repo_owner: str, repo_name: str, pr_number: int, event: Literal['APPROVE', 'REQUEST_CHANGES', 'COMMENT'], body: Optional[str] = None) -> Dict[str, Any]:
        """
        Submits a review for a specific pull request in a GitHub repository.
        Args:
            repo_owner (str): The owner of the repository.
            repo_name (str): The name of the repository.
            pr_number (int): The pull request number to review.
            event (Literal['APPROVE', 'REQUEST_CHANGES', 'COMMENT']): The type of review event.
            body (str, optional): Required when using REQUEST_CHANGES or COMMENT for the event parameter. Defaults to None.
        Returns:
            Dict[str, Any]: The JSON response from the GitHub API containing review information if successful.
            None: If an error occurs during the review submission process.
        Error Handling:
            Logs errors and prints the traceback if the review submission fails, returning None.
        """
        logging.info(f"Submitting review for PR {repo_owner}/{repo_name}#{pr_number}")
    
        # Construct the reviews URL
        reviews_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/pulls/{pr_number}/reviews"
    
        try:
            response = requests.post(reviews_url, headers=self._get_headers(), json={
                'body': body,
                'event': event
            }, timeout=TIMEOUT)
            response.raise_for_status()
            review_data = response.json()
    
            logging.info("Review submitted successfully")
            return review_data
    
        except Exception as e:
            logging.error(f"Error submitting review: {str(e)}")
            traceback.print_exc()
            return {"status": "error", "message": str(e)}
  • The registration code that dynamically registers all public methods of the GitHubIntegration instance (including 'update_reviews') as MCP tools by iterating over methods and calling mcp.add_tool() on each.
    def _register_tools(self):
        self.register_tools(self.gi)
        self.register_tools(self.ip)
    
    def register_tools(self, methods: Any = None) -> None:
        for name, method in inspect.getmembers(methods):
            if (inspect.isfunction(method) or inspect.ismethod(method)) and not name.startswith("_"):
                self.mcp.add_tool(method)
  • Import of the GitHubIntegration class, which contains the update_reviews handler, making it available for registration as an MCP tool.
    from .github_integration import GitHubIntegration as GI
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses that this is a mutation operation ('Submits a review'), describes error handling behavior (logs errors, returns None), and mentions the GitHub API response format. However, it doesn't cover important behavioral aspects like authentication requirements, rate limits, or whether the action is reversible.

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

Conciseness4/5

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

The description is well-structured with clear sections (Args, Returns, Error Handling) and front-loads the core purpose. While efficient, the error handling section could be more concise by integrating it with the Returns section rather than having separate headings.

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?

For a 5-parameter mutation tool with no annotations, the description provides good coverage: clear purpose, parameter semantics, return values, and error handling. The presence of an output schema means return values don't need detailed explanation. Minor gaps include lack of authentication/rate limit information and incomplete sibling differentiation.

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 description coverage, the description fully compensates by providing detailed parameter semantics. It explains each parameter's purpose, specifies when 'body' is required based on 'event' value, and documents default behavior. This adds significant value beyond the bare schema.

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 specific action ('Submits a review') on a specific resource ('for a specific pull request in a GitHub repository'). It distinguishes itself from siblings like 'add_pr_comments' or 'update_pr_description' by focusing on formal review submission with event types rather than general commenting or metadata updates.

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 context through parameter documentation (e.g., body is required for REQUEST_CHANGES or COMMENT events), but doesn't explicitly state when to use this tool versus alternatives like 'add_pr_comments' or 'merge_pr'. No guidance on prerequisites or when-not-to-use scenarios is provided.

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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