The MCP-Confirm server enables AI assistants to seek user confirmation and clarification through structured protocols. You can:
• Ask yes/no questions to verify user intent or clarify requests • Confirm actions before executing potentially impactful operations, including details on consequences • Clarify ambiguous requests by presenting multiple interpretation options • Verify understanding of user requirements and confirm planned next steps • Collect ratings to gather user feedback on AI responses or assistance quality • Create custom confirmation dialogs using specified JSON schemas for unique data collection needs • Search confirmation history with filtering by type, status, date range, and response time • Analyze confirmation data including success rates, response times, and trends with grouping capabilities
Provides example implementation for Node.js projects, enabling AI to confirm actions before project creation and manipulation.
Enables confirmation workflows when working with PostgreSQL databases, particularly in verification steps for database implementations.
Offers integration options for Python projects, allowing AI to clarify intent when users request Python project creation.
Supports React application development workflows, enabling AI to verify understanding before implementing features like user authentication.
@mako10k/mcp-confirm
An MCP server that implements confirmation protocols between AI and users. It provides tools for LLMs to request user confirmation when they need clarification or verification.
@mako10k/mcp-confirm
A Model Context Protocol (MCP) server for AI-user confirmation and clarification. This server provides tools for AI assistants (LLMs) to ask users for confirmation when they need clarification or verification.
Overview
This MCP server implements the Model Context Protocol Elicitation specification to enable confirmation and clarification protocols between AI assistants and users.
AI assistants can use this in situations such as:
- Confirming actions before execution
- Clarifying ambiguous requests
- Verifying understanding is correct
- Asking yes/no questions
- Collecting user satisfaction ratings
Features
Available Tools
- ask_yes_no
- Ask yes/no confirmation questions
- Used when AI needs clarification or verification
- confirm_action
- Confirm actions before execution
- Includes impact and details in confirmation dialog
- clarify_intent
- Clarify ambiguous requests
- Present multiple interpretation options
- verify_understanding
- Verify AI's understanding is correct
- Confirm next steps before proceeding
- collect_rating
- Collect user satisfaction ratings
- Evaluate AI response quality
- elicit_custom
- Custom confirmation dialogs
- Use custom JSON schemas
- search_logs
- Search confirmation history logs
- Filter by type, success status, date range, response time
- Paginated results for large datasets
- analyze_logs
- Statistical analysis of confirmation history
- Success rates, response times, trends
- Grouping by type, time period, etc.
Installation
Development with GitHub Codespaces
This project supports development with GitHub Codespaces:
- Open this repository on GitHub
- Click the "Code" button
- Select the "Codespaces" tab
- Click "Create codespace on main"
- The development environment will be set up automatically
For details, see DEVELOPMENT.md.
Configuration
Environment Variables
You can configure the server using environment variables:
MCP_CONFIRM_LOG_PATH
: Path to confirmation history log file (default:.mcp-data/confirmation_history.log
)MCP_CONFIRM_TIMEOUT_MS
: Default timeout for confirmations in milliseconds (default:180000
= 3 minutes)- Minimum:
5000
(5 seconds) - Maximum:
1800000
(30 minutes) - Invalid values will fall back to default with warning
- Minimum:
NODE_ENV
: Set todevelopment
to enable debug logging
Timeout Configuration Examples
Timeout Behavior
The server uses intelligent timeout settings based on confirmation type:
- Critical actions (delete, remove operations): 120 seconds
- Warning actions: 90 seconds
- Simple yes/no questions: 30 seconds
- Rating requests: 20 seconds (reference only)
- Other confirmations: 60 seconds (default)
Confirmation History Logging
All confirmation interactions are logged to a file for audit purposes. The log includes:
- Timestamp of the request
- Confirmation type
- Full request and response data
- Response time in milliseconds
- Success/failure status
- Error messages (if any)
The log directory (.mcp-data/
) will be created automatically if it doesn't exist.
VS Code Integration
Add to your .vscode/mcp.json
:
Claude Desktop Configuration
Add to your Claude Desktop config.json
:
Windows
Location: %APPDATA%\Claude\config.json
macOS
Location: ~/Library/Application Support/Claude/config.json
Linux
Location: ~/.config/claude/config.json
Usage Examples
Basic Confirmation
Intent Clarification
Understanding Verification
Technical Specifications
- Protocol: Model Context Protocol Elicitation
- Language: TypeScript
- Runtime: Node.js
- SDK Version: @modelcontextprotocol/sdk ^1.0.0
How It Works
This server implements true MCP Elicitation protocol:
- Sends
elicitation/create
method to client - Defines user input structure with JSON Schema
- User responds with
accept
,decline
, orcancel
- On
accept
, receives structured data following schema
This enables reliable communication between AI and users.
Development
License
MIT License - see LICENSE file for details.
Repository
- GitHub: https://github.com/mako10k/mcp-confirm
- npm: https://www.npmjs.com/package/@mako10k/mcp-confirm
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Tools
An MCP server implementing AI-user confirmation protocols, providing tools for LLMs to seek user confirmation when uncertain through yes/no questions, action confirmations, intent clarification, understanding verification, and satisfaction ratings.
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