Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agentic-debuggeradd logging to line 42 in cart.js to see why total is NaN"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
agentic-debugger
An MCP (Model Context Protocol) server that enables interactive debugging with code instrumentation for AI coding assistants. Inspired by Cursor's debug mode.
Works with any MCP-compatible AI coding tool:
Claude Code
Cursor
Windsurf
Cline
GitHub Copilot
Kiro
Zed
And more...
Features
Live code instrumentation - Inject debug logging at specific lines
Variable capture - Log variable values at runtime
Multi-language support - JavaScript, TypeScript, and Python
Browser support - CORS-enabled for browser JS debugging
Clean removal - Region markers ensure instruments are fully removed
Installation
Using npx (recommended)
Add to your MCP configuration:
{
"mcpServers": {
"debug": {
"command": "npx",
"args": ["-y", "agentic-debugger"]
}
}
}Configuration file locations:
Claude Code:
~/.mcp.jsonCursor:
.cursor/mcp.jsonin your project or~/.cursor/mcp.jsonOther tools: Check your tool's MCP documentation
Global install
npm install -g agentic-debuggerThen configure:
{
"mcpServers": {
"debug": {
"command": "agentic-debugger"
}
}
}Available Tools
Tool | Description |
| Start HTTP server for log collection |
| Stop server and cleanup |
| Insert logging code at file:line |
| Remove debug code from file(s) |
| Show all active instruments |
| Read captured log data |
| Clear the log file |
How It Works
Start session - Spawns a local HTTP server (default port 9876)
Add instruments - Injects
fetch()calls that POST to the serverReproduce bug - Run your code, instruments capture variable values
Analyze logs - Read the captured data to identify issues
Cleanup - Remove all instruments and stop the server
Debug Workflow Example
You: "Help me debug why the total is NaN"
AI Assistant:
1. Starts debug session
2. Reads your code to understand the logic
3. Adds instruments at suspicious locations
4. "Please run your code to reproduce the issue"
You: *runs code* "Done"
AI Assistant:
5. Reads debug logs
6. "I see `discount` is undefined at line 15..."
7. Removes instruments
8. Fixes the bug
9. Stops debug sessionInstrument Examples
JavaScript/TypeScript
// #region agentic-debug-abc123
fetch('http://localhost:9876/log', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
id: 'abc123',
location: 'cart.js:15',
timestamp: Date.now(),
data: { total, discount, items }
})
}).catch(() => {});
// #endregion agentic-debug-abc123Python
# region agentic-debug-abc123
try:
import urllib.request as __req, json as __json
__req.urlopen(__req.Request(
'http://localhost:9876/log',
data=__json.dumps({
'id': 'abc123',
'location': 'cart.py:15',
'timestamp': __import__('time').time(),
'data': {'total': total, 'discount': discount}
}).encode(),
headers={'Content-Type': 'application/json'}
))
except: pass
# endregion agentic-debug-abc123Supported Languages
Language | Extensions |
JavaScript |
|
TypeScript |
|
Python |
|
Requirements
Node.js >= 18.0.0
An MCP-compatible AI coding assistant
License
MIT