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UtakataKyosui

PR Review MCP Server

resolve_review_thread

Mark GitHub pull request review threads as resolved to close discussions and track feedback completion.

Instructions

Mark a review thread as resolved

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
thread_idYesReview thread ID (from list_review_threads)

Implementation Reference

  • The handler function that executes the resolve_review_thread tool: extracts thread_id from arguments, calls GitHubAPI.resolve_thread, formats success response with thread info.
    async def handle_resolve_review_thread(
        api: GitHubAPI, arguments: dict[str, Any]
    ) -> list[TextContent]:
        """Handle resolve_review_thread tool call."""
    
        thread_id = arguments["thread_id"]
    
        # Resolve thread
        thread = api.resolve_thread(thread_id)
    
        result = {
            "success": True,
            "thread": {"id": thread.get("id"), "is_resolved": thread.get("isResolved", False)},
        }
    
        return [TextContent(type="text", text=json.dumps(result, indent=2))]
  • Registers the resolve_review_thread tool with the MCP server in list_tools(), including name, description, and input schema.
    Tool(
        name="resolve_review_thread",
        description="Mark a review thread as resolved",
        inputSchema={
            "type": "object",
            "properties": {
                "thread_id": {
                    "type": "string",
                    "description": "Review thread ID (from list_review_threads)",
                }
            },
            "required": ["thread_id"],
        },
    ),
    Tool(
  • Defines the input schema for the resolve_review_thread tool: requires 'thread_id' string.
        inputSchema={
            "type": "object",
            "properties": {
                "thread_id": {
                    "type": "string",
                    "description": "Review thread ID (from list_review_threads)",
                }
            },
            "required": ["thread_id"],
        },
    ),
    Tool(
  • Helper method in GitHubAPI that performs the GraphQL mutation to resolve a review thread and returns the updated thread object.
    def resolve_thread(self, thread_id: str) -> dict[str, Any]:
        """
        Resolve a review thread.
    
        Args:
            thread_id: Review thread node ID
    
        Returns:
            Updated thread object
        """
        query = """
        mutation ResolveThread($threadId: ID!) {
            resolveReviewThread(input: {
                threadId: $threadId
            }) {
                thread {
                    id
                    isResolved
                }
            }
        }
        """
    
        variables = {"threadId": thread_id}
    
        data = self.execute_graphql(query, variables)
        thread = data.get("resolveReviewThread", {}).get("thread", {})
    
        if not thread:
            raise GitHubAPIError("Failed to resolve thread")
    
        return thread
  • Dispatcher in call_tool() that routes 'resolve_review_thread' calls to the handler function.
    elif name == "resolve_review_thread":
        return await handle_resolve_review_thread(api, arguments)
Behavior2/5

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

With no annotations provided, the description carries full burden but only states the basic action. It doesn't disclose behavioral traits like whether this is a destructive/mutative operation, permission requirements, rate limits, or what happens after resolution (e.g., thread locking). This leaves significant gaps for agent decision-making.

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 a single, efficient sentence with zero wasted words. It's appropriately sized for a simple tool and front-loads the core action, making it easy to parse.

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 mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'resolved' means in context, whether the action is reversible, what the response looks like, or how it differs from sibling tools, leaving the agent with incomplete operational context.

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 100%, so the schema already documents the single parameter 'thread_id' with its source. The description adds no additional parameter information beyond what's in the schema, meeting the baseline for high coverage.

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 the action ('Mark as resolved') and resource ('review thread'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'reply_and_resolve' which might have overlapping functionality, preventing a perfect score.

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?

No guidance is provided on when to use this tool versus alternatives like 'reply_and_resolve' or 'reply_to_review_thread'. The description only states what it does, not when it's appropriate or what prerequisites might exist.

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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