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AstroMined

PyGithub MCP Server

by AstroMined

add_issue_comment

Add comments to GitHub issues by specifying repository details and issue number. This tool enables users to provide feedback, answer questions, or update status on GitHub issues directly through the PyGithub MCP Server.

Instructions

Add a comment to an issue.

Args:
    params: Parameters for adding a comment including:
        - owner: Repository owner (user or organization)
        - repo: Repository name
        - issue_number: Issue number to comment on
        - body: Comment text

Returns:
    Created comment details from GitHub API

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Implementation Reference

  • The @tool decorated handler function for 'add_issue_comment' that validates input, calls the operations layer, handles errors, and formats the MCP response.
    @tool()
    def add_issue_comment(params: IssueCommentParams) -> dict:
        """Add a comment to an issue.
        
        Args:
            params: Parameters for adding a comment including:
                - owner: Repository owner (user or organization)
                - repo: Repository name
                - issue_number: Issue number to comment on
                - body: Comment text
        
        Returns:
            Created comment details from GitHub API
        """
        try:
            logger.debug(f"add_issue_comment called with params: {params}")
            # Pass the Pydantic model directly to the operation
            result = issues.add_issue_comment(params)
            logger.debug(f"Got result: {result}")
            return {"content": [{"type": "text", "text": json.dumps(result, indent=2)}]}
        except GitHubError as e:
            logger.error(f"GitHub error: {e}")
            return {
                "content": [{"type": "error", "text": format_github_error(e)}],
                "is_error": True
            }
        except Exception as e:
            logger.error(f"Unexpected error: {e}")
            logger.error(traceback.format_exc())
            error_msg = str(e) if str(e) else "An unexpected error occurred"
            return {
                "content": [{"type": "error", "text": f"Internal server error: {error_msg}"}],
                "is_error": True
            }
  • Pydantic model defining the input schema for the add_issue_comment tool, inheriting from RepositoryRef (owner/repo) with issue_number and body fields, including validation.
    class IssueCommentParams(RepositoryRef):
        """Parameters for adding a comment to an issue."""
    
        model_config = ConfigDict(strict=True)
        
        issue_number: int = Field(..., description="Issue number to comment on")
        body: str = Field(..., description="Comment text")
        
        @field_validator('body')
        @classmethod
        def validate_body(cls, v):
            """Validate that body is not empty."""
            if not v.strip():
                raise ValueError("body cannot be empty")
            return v
  • The register function that adds add_issue_comment to the list of issue tools and registers them all with the MCP server using register_tools.
    def register(mcp: FastMCP) -> None:
        """Register all issue tools with the MCP server.
        
        Args:
            mcp: The MCP server instance
        """
        from pygithub_mcp_server.tools import register_tools
        
        # List of all issue tools to register
        issue_tools = [
            create_issue,
            list_issues,
            get_issue,
            update_issue,
            add_issue_comment,
            list_issue_comments,
            update_issue_comment,
            delete_issue_comment,
            add_issue_labels,
            remove_issue_label,
        ]
        
        register_tools(mcp, issue_tools)
        logger.debug(f"Registered {len(issue_tools)} issue tools")
  • The operations layer function that executes the core GitHub API logic using PyGithub to create the issue comment and convert the response.
    def add_issue_comment(params: IssueCommentParams) -> Dict[str, Any]:
        """Add a comment to an issue.
    
        Args:
            params: Validated parameters for adding a comment
    
        Returns:
            Created comment details from GitHub API
    
        Raises:
            GitHubError: If the API request fails
        """
        try:
            client = GitHubClient.get_instance()
            repository = client.get_repo(f"{params.owner}/{params.repo}")
            issue = repository.get_issue(params.issue_number)
            comment = issue.create_comment(params.body)
            return convert_issue_comment(comment)
        except GithubException as e:
            raise GitHubClient.get_instance()._handle_github_exception(e)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the action ('Add a comment') but doesn't mention authentication requirements, rate limits, error conditions, or what 'Created comment details from GitHub API' includes. For a write operation with zero annotation coverage, this leaves significant gaps in understanding the tool's behavior.

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 (purpose, args, returns) and uses bullet points for parameters. It's appropriately sized for the tool's complexity. The only minor inefficiency is repeating 'Parameters for adding a comment' in both the main description and the args section.

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?

For a write operation with no annotations, no output schema, and 4 parameters, the description is incomplete. It doesn't cover authentication needs, error handling, rate limits, or what the return value contains beyond 'Created comment details from GitHub API'. The agent lacks crucial information for reliable tool invocation.

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 description coverage is 0%, so the description must compensate. It lists all four parameters (owner, repo, issue_number, body) with brief explanations that match what would be in a good schema. However, it doesn't provide additional context like format requirements (e.g., owner must be valid GitHub user/org), constraints, or examples beyond the basic definitions.

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 'Add a comment to an issue' which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'update_issue_comment' or 'delete_issue_comment' by focusing on creation rather than modification or deletion. However, it doesn't explicitly differentiate from 'list_issue_comments' which is a read operation.

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. There's no mention of prerequisites (e.g., authentication requirements), when not to use it, or how it relates to sibling tools like 'update_issue_comment' or 'create_issue'. The agent must infer usage from the tool name alone.

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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