LLM Debug Context MCP - Startup Idea

Problem (Pain Score: 7/10)

When asking AI coding assistants for debugging help, LLMs only see static code—they can’t access actual runtime state.

Real Examples:

• “I don’t know why this variable is null” → LLM can only guess
• Repeatedly copy-pasting stack traces for complex bugs
• LLM suggestions don’t work at runtime
• Limitations of logic analysis without actual variable values

Frequency: Every debugging session (daily)

AI coding tools like Claude Code and Cursor have become powerful, but complex bug resolution has limits without runtime context.

Target Market

Primary Targets:

• AI coding tool users (Claude Code, Cursor, Copilot)
• Backend developers needing complex debugging
• VS Code/IDE power users
• MCP ecosystem early adopters

Market Size:

• TAM: $82.1B (LLM market, 2033)
• AI coding tool users: 81% (GitHub survey)
• MCP ecosystem: rapidly growing

Customer Characteristics:

• Actively uses AI coding tools
• Spends significant time debugging
• Interested in new dev tools
• Willing to invest in productivity

Proposed Solution

Core Features:

1. DAP (Debug Adapter Protocol) Integration

   • Connects with VS Code debugger
   • Captures breakpoint state
   • Captures variable values, call stack

2. MCP Server

   • Direct use from Claude Code/Desktop
   • Standard MCP protocol compliance
   • Tool calls to query debug info

3. Context Formatting

   • LLM-friendly runtime state formatting
   • Filter to relevant variables only (noise reduction)
   • Stack trace summarization

4. Interactive Debugging

   • LLM can directly issue step over/into commands
   • Conditional breakpoint suggestions
   • Test variable value changes

Competitive Analysis

Differentiation:

• MCP native (direct Claude Code integration)
• Multi-language support (via DAP standard)
• LLM-friendly context formatting
• Interactive debugging commands

MVP Development Plan

Timeline: 5 weeks

Week 1: DAP Integration

• Debug Adapter Protocol client
• VS Code debugger connection
• Basic state collection

Week 2: MCP Server

• MCP server framework
• Tool definitions (get_variables, get_stack, etc.)
• Claude Code integration testing

Week 3: Context Processing

• LLM-friendly formatting
• Variable filtering logic
• Summary generation

Week 4: Interaction

• Step command implementation
• Breakpoint management
• Error handling

Week 5: Launch

• npm/pip package deployment
• Documentation and examples
• MCP server registry registration

Tech Stack:

• Runtime: TypeScript (MCP SDK)
• Protocol: DAP (Debug Adapter Protocol)
• Target: VS Code debugger

Revenue Model

Pricing:

Revenue Projections:

• Year 1 target: $2K MRR
• 150 paid customers (avg $13/mo)
• Scale with MCP ecosystem growth

Growth Strategy:

• Register in MCP server directories
• AI coding tool community marketing
• Target Claude Code users

Risks & Challenges

Technical Risks:

• DAP integration complexity
• Supporting various languages/runtimes

Market Risks:

• Anthropic/Microsoft may implement directly
• MCP ecosystem uncertainty

Operational Risks:

• Debugger environment diversity
• Security (runtime data exposure)

Mitigation:

• Start with common languages (Python, JS)
• Local-only addresses security concerns
• Ride MCP ecosystem growth

Why We Recommend This

Score: 85/100

1. Clear pain point: Lack of runtime context in AI debugging
2. Market gap: No MCP-based debugger tools exist
3. Growing ecosystem: MCP, Claude Code rapid growth
4. Preferred domain: dev_tools
5. Reasonable MVP timeline: 5 weeks
6. High technical differentiation: DAP + MCP combination

An opportunity to pioneer runtime-aware debugging, the next step in AI coding tools.