What is Codehooks Mcp Server

Codehooks.io MCP Server is a Model Context Protocol server that enables AI agents to perform various operations such as database management, serverless code deployment, and file handling on the Codehooks.io platform. It provides a comprehensive set of tools for building scalable applications through easy interaction with AI.

Use cases

Common use cases include building systems like survey platforms, inventory trackers, webhook processing pipelines, content management systems, and automated data backup solutions. These applications utilize the server’s capabilities to manage databases, deploy APIs, and handle file storage and retrieval efficiently.

How to use

To use the MCP server, set up the environment by creating a script for running Docker containers with your project name and admin token. Configure your local setups such as Claude Desktop or Cursor with the appropriate commands to start the server. Then, interact with the server through natural language requests, prompting it to perform specific tasks related to database operations, code deployment, or file management.

Key features

Key features include the ability to query and manage databases, deploy serverless JavaScript functions, manage files in cloud storage, and implement key-value storage solutions. It also supports creating collections, adding schemas and indexes, and automating processes such as data backup and restoration.

Where to use

The MCP server can be used in a wide variety of environments including web applications, mobile app backends, and serverless architectures. It is ideal for developers looking to enhance their applications with dynamic data management capabilities without managing their own server infrastructure.

Codehooks.io MCP Server

An MCP (Model Context Protocol) server that provides AI agents with database operations, serverless code deployment, and file management capabilities on the Codehooks.io platform.

Available functionality

Database & Collections

• Query and update collections (including metadata) with filters and sorting
• Create and manage collections
• Import/export data (JSON,JSONL,CSV)
• Add schemas and indexes, cap collections

Code Deployment

• Deploy JavaScript serverless functions

File Operations

• Upload files to cloud storage
• List and browse files
• Delete files
• Inspect file metadata

Key-Value Store

• Store key-value pairs
• Retrieve one or many key-value pairs
• Delete key-value pairs
• Set time-to-live (TTL) for key-value pairs

System Operations

• View application logs
• Access API documentation (local documentation for the MCP agent)

Setup

Get Codehooks Admin Token (keep it secret!)

coho login
coho add-admintoken

Create MCP Server Script

Create a folder for your MCP server scripts:

mkdir ~/mcp-servers
cd ~/mcp-servers

For macOS/Linux - Create codehooks.sh:

#!/bin/bash

# Set PATH to include common Docker locations
export PATH="/usr/local/bin:/opt/homebrew/bin:/usr/bin:/bin:$PATH"

exec docker run --rm -i \
  -e CODEHOOKS_PROJECT_NAME=your_project_name \
  -e CODEHOOKS_ADMIN_TOKEN=your_admin_token \
  -e CODEHOOKS_SPACE=your_space_name \
  ghcr.io/restdb/codehooks-mcp:latest

Make it executable:

chmod +x ~/mcp-servers/codehooks.sh

For Windows - Create codehooks.bat:

@echo off
docker run --rm -i ^
  -e CODEHOOKS_PROJECT_NAME=your_project_name ^
  -e CODEHOOKS_ADMIN_TOKEN=your_admin_token ^
  -e CODEHOOKS_SPACE=your_space_name ^
  ghcr.io/restdb/codehooks-mcp:latest

Replace your_project_name, your_admin_token, and your_space_name with your actual values.

Configure for Claude Desktop

Add to your claude_desktop_config.json:

macOS/Linux:

{
  "mcpServers": {
    "codehooks": {
      "command": "/Users/username/mcp-servers/codehooks.sh"
    }
  }
}

Windows:

{
  "mcpServers": {
    "codehooks": {
      "command": "C:\\Users\\username\\mcp-servers\\codehooks.bat"
    }
  }
}

Configure for Cursor

Add to your ~/.cursor/mcp.json:

macOS/Linux:

{
  "mcpServers": {
    "codehooks": {
      "command": "/Users/username/mcp-servers/codehooks.sh"
    }
  }
}

Windows:

{
  "mcpServers": {
    "codehooks": {
      "command": "C:\\Users\\username\\mcp-servers\\codehooks.bat"
    }
  }
}

Replace username with your actual username.

Example Requests

• “Build a complete survey system: create a database, deploy an API to collect responses, and add search/analytics endpoints”
• “Set up a real-time inventory tracker: import my product CSV, create stock update webhooks, and build low-stock alerts”
• “Build a webhook processing pipeline: receive webhooks from multiple sources, transform and validate data, then trigger automated actions”
• “Build a content management system: create file upload endpoints, set up a metadata database, and deploy content delivery APIs”
• “Set up automated data backups: export my collections to JSON files, store them with timestamps, and create restoration endpoints”

How These Examples Work

Complete Survey System

The AI agent would:

1. Create collections (surveys, responses) for data storage
2. Add schemas for data validation and structure
3. Deploy JavaScript endpoints like POST /surveys and GET /surveys/:id/analytics
4. Create indexes on response fields for fast searching and analytics

Real-time Inventory Tracker

The AI agent would:

1. Import your CSV to populate the products collection
2. Deploy webhook handlers for POST /inventory/update and GET /inventory/low-stock
3. Set up key-value storage for alert thresholds and settings
4. Create indexes on SKU and stock levels for real-time queries

Webhook Processing Pipeline

The AI agent would:

1. Deploy webhook receivers like POST /webhooks/stripe and POST /webhooks/github
2. Create collections for webhook_logs, processed_events, and failed_events
3. Set up data transformation rules and validation schemas for each webhook source
4. Use key-value store for rate limiting and duplicate detection with TTL
5. Deploy action triggers that send emails, update databases, or call other APIs based on webhook data

Content Management System

The AI agent would:

1. Create collections for content, media, and users
2. Deploy file upload endpoints with POST /upload and GET /content/:id
3. Upload and manage static files for content delivery
4. Store metadata linking files to content records with search indexes

Automated Data Backups

The AI agent would:

1. Export collections to JSON format with timestamps
2. Upload backup files to cloud storage automatically
3. Deploy restoration APIs like GET /backups and POST /restore/:backup-id
4. Store backup metadata in key-value store for tracking and management

Each example demonstrates how multiple MCP tools work together to create complete, production-ready systems through natural conversation with your AI agent.