Igniter.js MCP Server

Overview

The Igniter.js MCP Server transforms your project into an AI-native development platform. Unlike traditional frameworks that were built for humans, Igniter.js was designed from the ground up to be understood and utilized by AI agents through the Model Context Protocol (MCP).

Think of it as giving your AI coding assistant X-ray vision into your entire project. Instead of making educated guesses about your code, AI agents can actually ask your codebase questions and get real, accurate answers.

What Makes This Revolutionary?

Most AI coding tools today are sophisticated autocomplete systems. They pattern-match against billions of lines of code to predict what you probably want to write next. But with Igniter.js MCP Server, your AI agent develops actual understanding of your project architecture, data flow, business logic, and the relationships between features.

This isn't just about code completion—it's about having an intelligent conversation with your codebase.

Tools

The Igniter.js MCP Server exposes a comprehensive set of tools that give AI agents deep insight into your project:

Development Lifecycle

  • start_dev_server: Start the development server with live reloading and client generation
  • build_project: Compile your project for production deployment
  • run_tests: Execute your test suite with filtering and watch options
  • generate_schema: Generate type-safe client from your API router
  • generate_docs: Create OpenAPI documentation with interactive UI

Code Intelligence

  • analyze_file: Deep analysis of file structure, imports, exports, and TypeScript errors
  • analyze_feature: Comprehensive feature analysis with statistics and health metrics
  • find_implementation: Locate where symbols, functions, and classes are implemented
  • explore_source: Detailed exploration of code with context and dependencies
  • trace_dependency_chain: Map complete dependency chains from usage to implementation

Scaffolding Tools

  • generate_feature: Scaffold complete feature modules with controllers and procedures
  • generate_controller: Create new controllers within existing features
  • generate_procedure: Generate middleware procedures for reusable logic

Project Management

  • create_task: Create development tasks with priorities and assignments
  • list_tasks: View and filter tasks by status, priority, or assignee
  • update_task_status: Track progress and completion
  • get_task_statistics: Analyze workload and performance metrics

AI Agent Collaboration

  • delegate_to_agent: Delegate tasks to specialized agents (Claude, Gemini, GPT)
  • check_delegation_status: Monitor progress of delegated work
  • monitor_agent_tasks: Real-time monitoring with logs and analytics
  • find_delegation_candidates: Identify tasks suitable for agent delegation

Memory and Knowledge

  • store_memory: Persist insights, patterns, and architectural decisions
  • search_memories: Query stored knowledge with filters and tags
  • relate_memories: Create relationships between different knowledge types
  • visualize_memory_graph: Generate diagrams showing knowledge connections

GitHub Integration

  • create_github_issue: Create issues with templates and labels
  • search_github_issues: Find and filter issues across repositories
  • get_pull_request: Analyze PR details, changes, and status
  • search_code: Search code across GitHub repositories

Sample Prompts

Once connected to your MCP-compatible AI agent, you can have natural conversations about your project:

Project Analysis

  • "Analyze the user authentication feature and show me any potential issues"
  • "What are all the API endpoints and their current health status?"
  • "Show me the dependency chain for the user management system"

Development Tasks

  • "Generate a new user management feature with authentication"
  • "Create a controller for handling payment processing in the billing feature"
  • "Build the project for production and show me any errors"

Code Understanding

  • "Explain how the payment processing flow works in this codebase"
  • "What would break if I refactor the User model to use a different ID type?"
  • "Show me all the places where authentication might fail"

Collaborative Development

  • "Delegate the API testing task to a Claude agent and monitor progress"
  • "What patterns has the team learned about error handling in this project?"
  • "Store this architectural decision about why we chose this database structure"

Integrating with AI Tools

AI tools have different ways of integrating MCP servers. The configuration contains a command for starting the server that'll be executed by the respective tool.

Cursor

Add via Cursor Settings UI

  • Open Cursor Settings
  • Select MCP in the settings sidenav
  • Click + Add new global MCP server
  • Add the Igniter.js configuration:
Code
{
  "mcpServers": {
    "Igniter": {
      "command": "npx",
      "args": ["@igniter-js/mcp-server"]
    }
  }
}

Project Configuration

For project-specific configuration, create .cursor/mcp.json in your project root:

Code
{
  "mcpServers": {
    "Igniter": {
      "command": "npx", 
      "args": ["@igniter-js/mcp-server"],
      "env": {
        "IGNITER_PROJECT_ROOT": "."
      }
    }
  }
}

VS Code Copilot

Create .vscode/mcp.json in your workspace:

Code
{
  "servers": {
    "Igniter": {
      "command": "npx",
      "args": ["@igniter-js/mcp-server"]
    }
  }
}

Windsurf

Add via Windsurf Settings > Cascade > Add Server:

Code
{
  "mcpServers": {
    "Igniter": {
      "command": "npx",
      "args": ["@igniter-js/mcp-server"]  
    }
  }
}

Claude Desktop

Create the configuration file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
Code
{
  "mcpServers": {
    "Igniter": {
      "command": "npx",
      "args": ["@igniter-js/mcp-server"]
    }
  }
}

Claude Code (Terminal)

Code
claude mcp add igniter npx @igniter-js/mcp-server

Environment Variables

Configure these environment variables for enhanced functionality:

Code
# For agent delegation capabilities
ANTHROPIC_API_KEY=your_claude_key
GOOGLE_API_KEY=your_gemini_key  
OPENAI_API_KEY=your_gpt_key

# For GitHub integration
GITHUB_TOKEN=your_github_token

# For memory and task persistence
DATABASE_URL=your_database_connection

The AI-Native Advantage

Beyond Code Completion

Traditional AI coding assistants are pattern-matching systems. Igniter.js MCP Server enables true understanding:

  • Architectural Awareness
    Your AI agent understands your project's structure and design decisions
  • Business Logic Context
    It knows your domain rules and can apply them to new features
  • Impact Analysis
    It can predict what breaks when you change something
  • Memory Persistence
    It remembers your conversations and architectural decisions

Multi-Agent Collaboration

Why stop at one AI agent? With Igniter.js MCP Server, you can orchestrate teams of specialized agents:

  • Research Agents that excel at understanding requirements
  • Implementation Agents that write clean, maintainable code
  • Testing Agents that focus on quality assurance
  • Documentation Agents that keep everything up to date

Each agent has access to the same rich context about your project but can focus on what they do best.

The Network Effect

As more developers use Igniter.js with MCP, the entire ecosystem becomes smarter. Successful patterns get reinforced, common problems get solved once and shared, and the framework evolves based on real usage from thousands of AI-assisted development sessions.

Getting Started Today

The future of AI-native development isn't some distant vision—it's available right now. Every Igniter.js starter comes with MCP Server pre-configured and ready to use with your favorite AI coding assistant.

Create your first AI-native project:

Code
npx create-igniter@latest my-app
cd my-app
npm run dev

Then connect your preferred AI agent and start having real conversations with your codebase.

Real-World Impact

Week 1: Instead of spending hours understanding authentication patterns, ask your AI agent. It analyzes your codebase and explains the architectural decisions and security considerations.

Month 1: Need to refactor a critical component? Your AI agent predicts exactly what will break, suggests migration strategies, and helps write the migration scripts.

Month 3: New team member joins? Your AI agent gives them a personalized tour of the codebase, explaining business logic and architectural decisions specific to your project.

Month 6: Planning a major feature? Your AI agent analyzes the entire codebase, identifies potential conflicts, suggests improvements, and helps break work into manageable tasks.

This isn't about replacing developers—it's about giving developers superpowers. It's about turning every development session into a collaborative conversation with intelligent agents who understand your code as well as you do.

The age of AI-native development has begun. Start your journey with Igniter.js today and experience the future of collaborative coding.