About codify-mcp
codify-mcp is an MCP server for exposing reusable browser automations as tools, aimed at AI assistant users and automation builders. Tools are supplied as JSON arguments and run through Apify Agent.
Key features
- 01Expose custom tools through MCP
- 02Run Playwright browser scripts
- 03Execute Apify actors
- 04Define inputs with JSON schemas
- 05Pass one or multiple tools at launch
Use cases
- 01Automate product-page data extraction
- 02Reuse browser actions as assistant tools
- 03Run several defined automations in a deployment
MCP support
The MCP server registers supplied automation definitions as tools for MCP clients. Implementations can use Playwright scripts or Apify actors.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMECodify MCP
MCP server for custom automation tools using Apify Agent
Part of Project Codify
Project Codify
Complete end-to-end browser automation pipeline running inside browser (or locally). Codify and replay browser actions as robust (deterministic) and reusable scripts. From rapid prototyping and quick automation scripts to sophisticated deployments at scale.
-
Login - reusable authentication sessions - reusable secure login (TBD) ❎
-
Agent - turn scripts into browser actions - code or text script (future) ✅
-
Coder - turn browser actions into scripts (TBD - currently integrated) ✅
-
Robot - automation engine for scalability (TBD - currently integrated) ✅
-
MCP - automations become reusable tools you can reuse through AI ✅
More is on the way... Give it a shot 🎬 or join the list to follow the project! 🔔
What It Does
Model Context Protocol (MCP) server that lets AI assistants (e.g. Claude, Cursor, VS Code) execute browser automation tasks via Apify Agent. Tools are passed as clean JSON arguments — one per line. No manual setup or file creation.
Usage
Run directly using npx:
npx codify-mcp {{JSON_MCP_TOOL_1}} {{JSON_MCP_TOOL_2}} {{JSON_MCP_TOOL_3}}...
Or install locally:
npm install -g codify-mcp
Quick Start
1. Apify Token
Optional - you can also place the token inside the MCP server JSON later.
apify login
This saves your token to ~/.apify/auth.json.
Alternatively, set the environment variable:
export APIFY_TOKEN="your_token_here"
2. Create a Tool
Apify Coder can turn casual browser actions into reusable AI tools. Currently, this feature is also integrated in Apify Agent
Export a tool and append the result as a JSON string to the MCP server args field or ask your AI to do it for you.
{
"name": "scrape_product",
"description": "Scrape product info from a page",
"inputSchema": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "Product page URL"
}
},
"required": ["url"]
},
"implementation": {
"type": "apify-actor",
"actorId": "cyberfly/apify-agent",
"script": "await page.goto(inputs.url); const title = await page.textContent('h1'); return {title};"
}
}
3. Run the Server
npx codify-mcp '{"name":"scrape_product","description":"...","inputSchema":{...},"implementation":{...}}'
Or with multiple tools:
npx codify-mcp \
'{"name":"tool1",...}' \
'{"name":"tool2",...}' \
'{"name":"tool3",...}'
4. Connect to Claude Desktop (or Cursor/VS Code)
Edit your Claude Desktop config:
macOS/Linux: ~/.config/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"codify-mcp": {
"command": "npx",
"args": [
"codify-mcp",
"{\"name\":\"scrape_product\",\"description\":\"...\",\"inputSchema\":{...},\"implementation\":{...}}"
],
"env": {
"APIFY_TOKEN": "your_token_or_leave_empty_to_use_auth_file"
}
}
}
}
Restart Claude. Your tools are now available to the AI assistant.
Tool Definition Reference
Basic Structure
{
// Required
"name": "tool_name", // alphanumeric + underscore/dash
"description": "What the tool does", // shown to AI
"inputSchema": {
"type": "object",
"properties": {
"paramName": {
"type": "string",
"description": "Parameter description"
}
},
"required": ["paramName"]
},
"implementation": {
"type": "apify-actor",
"actorId": "cyberfly/apify-agent", // actor to run
"script": "await page.goto(inputs.url); ..." // Playwright code
},
// Optional
"version": "1.0.0",
"metadata": { "custom": "fields" }
}
Implementation Details
- script: Playwright automation code. Receives
inputsobject with user-provided parameters andpageobject for browser automation. - actorId: Apify actor to execute. Defaults to
cyberfly/apify-agent.
Input Schema Examples
Simple text input:
{
"url": {
"type": "string",
"description": "Website URL"
}
}
Optional field:
{
"timeout": {
"type": "integer",
"description": "Timeout in seconds",
"default": 30
}
}
Enum (dropdown):
{
"format": {
"type": "string",
"enum": ["json", "csv", "markdown"],
"description": "Output format"
}
}
Usage Patterns
Single Tool (Development)
npx codify-mcp '{"name":"test","description":"Test tool","inputSchema":{"type":"object","properties":{}},"implementation":{"type":"apify-actor","script":"console.log('hello')"}}'
Multiple Tools (Production)
npx codify-mcp \
"$(cat tools/scraper.json)" \
"$(cat tools/logger.json)" \
"$(cat tools/analyzer.json)"
With Environment Variable
APIFY_TOKEN="apk_..." npx codify-mcp '{"name":"...","description":"...","inputSchema":{},"implementation":{"type":"apify-actor","script":"..."}}'
With npm link (Local Testing)
cd /path/to/codify-mcp
npm link
# Now use anywhere
codify-mcp '{"name":"...","description":"...","inputSchema":{},"implementation":{"type":"apify-actor","script":"..."}}'
Authentication
Token resolution order:
- APIFY_TOKEN environment variable (if set and not empty)
- ~/.apify/auth.json (from
apify loginCLI command) - Error: No token found, tool execution will fail with clear message
Troubleshooting
"No valid tools in arguments"
Ensure you're passing valid JSON strings as arguments:
# ✓ Correct
npx codify-mcp '{"name":"test","description":"Test","inputSchema":{"type":"object","properties":{}},"implementation":{"type":"apify-actor","script":"return {ok:true}"}}'
# ✗ Wrong (missing quotes around JSON)
npx codify-mcp {name:"test"...}
# ✗ Wrong (single quotes around JSON on Linux/Mac may need escaping)
npx codify-mcp '{name:"test"...}' # Use double quotes inside
"Invalid or missing Apify token"
Ensure authentication is set up:
# Option 1: Login via CLI
apify login
# Option 2: Set environment variable
export APIFY_TOKEN="apk_your_token_here"
apify token
"Tool execution failed"
Check your Playwright script syntax. The script must be valid JavaScript that:
- Has access to
inputs(user-provided parameters) - Has access to
page(Playwright page object) - Returns a value or object
// ✓ Valid
await page.goto(inputs.url);
const title = await page.textContent('h1');
return { title };
// ✗ Invalid (missing await)
page.goto(inputs.url);
Large Tool Sets (50+ tools)
If you have many tools, consider splitting into multiple MCP servers:
{
"mcpServers": {
"apify-scraper": {
"command": "npx",
"args": ["codify-mcp", "...tool1...", "...tool2..."]
},
"apify-analyzer": {
"command": "npx",
"args": ["codify-mcp", "...tool3...", "...tool4..."]
}
}
}
Development
Running Locally
npm link
codify-mcp '{"name":"test",...}'
Structure
lib/
index.js # Main entry: assembleWrapperCode(), start()
mcp/
resolver.js # Module path bootstrapping
auth.js # Token resolution
actor_caller.js # Apify actor execution
server_setup.js # MCP server + tool registration
bin/
start.js # Executable entry point (bin field in package.json)
Key Design Principles
- No files: Tools passed entirely via argv; no config files or manual setup.
- No base64: Clean, readable command lines; no obfuscation.
- Self-contained: All dependencies bundled; works offline once installed.
- Stateless: Each invocation is independent; easy horizontal scaling.
- Token from env/CLI: Seamless auth experience; respects Apify ecosystem conventions.
License
Apache-2.0
Contributing
Issues and PRs welcome at github.com/cybairfly/codify-mcp
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.