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.
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.
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.
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
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.
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.
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
NODE_ENV: Set to development to enable debug logging
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)
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.
Add to your .vscode/mcp.json:
Add to your Claude Desktop config.json:
Location: %APPDATA%\Claude\config.json
Location: ~/Library/Application Support/Claude/config.json
Location: ~/.config/claude/config.json
Protocol: Model Context Protocol Elicitation
Language: TypeScript
Runtime: Node.js
SDK Version: @modelcontextprotocol/sdk ^1.0.0
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, or cancel
On accept, receives structured data following schema
This enables reliable communication between AI and users.
MIT License - see LICENSE file for details.
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.
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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