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Development

Overview

This repository (public) provides:

  • The core MCP server implementation (published as an NPM package)
  • The stdio entry point (CLI)
  • An Express HTTP server for local development and testing

The hosted server (mcp.apify.com) is implemented in an internal Apify repository that depends on this package.

For general information about the Apify MCP Server, features, tools, and client setup, see the README.md.

Project structure (high-level)

src/
  mcp/          MCP protocol implementation
  tools/        MCP tool implementations
  resources/    Resources and widgets metadata
  utils/        Shared utilities
  web/          React UI widgets (built into dist/web)
tests/
  unit/         Unit tests
  integration/  Integration tests

Key entry points:

  • src/index.ts - Main library export (ActorsMcpServer class)
  • src/index_internals.ts - Internal exports for testing / advanced usage
  • src/stdio.ts - Standard input/output (CLI) entry point
  • src/dev_server.ts - Express HTTP server for local development (npm start)
  • src/input.ts - Input processing and validation

Tool loading phases

Tool loading is intentionally split into two phases in src/utils/tools_loader.ts:

  • getActors() — async, mode-agnostic. Fetches Actor metadata and preserves the caller's requested tool/actor selection without choosing any mode-dependent tool variants.
  • getToolsForServerMode() — sync, mode-dependent. Takes the pre-fetched sources plus a resolved ServerMode and produces the concrete tool entries to expose to the client.
  • loadToolsFromInput() in tools_loader.ts — convenience wrapper running both phases back-to-back with an explicit ServerMode. Not to be confused with ActorsMcpServer.loadToolsFromInput() (the public method), which queues sources when mode is still 'auto' and registers them onto the server — call the server method from transport entry points, the plain function only when you already have a resolved mode.

This split matters for serverMode: 'auto'.

  • Before initialize, the server does not yet know whether the client supports MCP Apps.
  • Public preload helpers such as ActorsMcpServer.loadToolsByName() and loadToolsFromUrl() therefore queue mode-agnostic sources first.
  • Actor tools may still be loaded immediately because they are mode-agnostic.
  • During initialize, once client capabilities are known, the server resolves the queued sources into the final mode-dependent tool set.

Rule of thumb:

  • If code may run before initialize in auto mode, it must stay in the mode-agnostic phase.
  • Only code running after mode resolution should call getToolsForServerMode() or otherwise choose concrete mode-dependent tool variants.

Node.js version policy

The minimum supported Node.js version is 20 (engines.node >= 20 in package.json).

Why Node.js 20:

@segment/analytics-node (used for telemetry) declares engines: { node: ">=20" }, which makes Node.js 20 the hard floor for this package.

  • The .nvmrc file pins the latest Node.js version for development tooling (lint, type-check, build) — this is intentionally higher than the minimum supported version.

How to contribute

Refer to the CONTRIBUTING.md file.

Installation

npm install
cd src/web && npm install

Working on the MCP Apps (ChatGPT Apps) UI widgets

Widget code lives in src/web/ (a self-contained React project). Widgets are rendered based on tool output — to add data to a widget, modify the corresponding tool's return value.

UI mode: Widget rendering requires the server to run in UI mode. Use ?ui=true (e.g., /mcp?ui=true) or set UI_MODE=true.

See the OpenAI Apps SDK documentation for background on MCP Apps and widgets.

Production build

npm run build

Builds the core TypeScript project and src/web/ widgets, then copies widgets into dist/web/. Required before running integration tests or the compiled server.

Hot-reload development

APIFY_TOKEN='your-apify-token' npm run dev

Starts the web widgets builder in watch mode and the MCP server in standby mode on port 3001. Editing src/web/src/widgets/*.tsx triggers a hot-reload — the next widget render uses updated code without restarting the server. Adding new widget filenames requires reconnecting the MCP client to pick them up.

  • Get your APIFY_TOKEN from Apify Console
  • Preview widgets via the local esbuild dev server at http://localhost:3226/index.html

The MCP server listens on port 3001. The HTTP server implementation is in src/dev_server.ts. The hosted production server behind mcp.apify.com is located in the internal Apify repository.

Configuring APIFY_TOKEN for Claude Code

Create or edit .claude/settings.local.json:

{
  "env": {
    "APIFY_TOKEN": "<YOUR_APIFY_API_TOKEN>"
  }
}

Restart Claude Code for the change to take effect. This token is picked up by both Claude Code MCP servers (defined in .mcp.json) and mcpc.

Testing

Layer Command What it covers
Unit tests npm run test:unit Individual modules in isolation — no credentials needed
Integration tests npm run test:integration Full server over all transports against real Apify API (requires APIFY_TOKEN + npm run build)
mcpc probing mcpc @stdio tools-call ... Interactive end-to-end verification during development
LLM evals CI only — apply validated label Runs evals/run_evaluation.ts against multiple models via OpenRouter; requires PHOENIX_* and OPENROUTER_* secrets

To trigger the eval workflow on a PR, apply the validated label. The workflow then runs automatically and posts results to Phoenix. It also runs automatically on every merge to the master branch.

Test structure

  • tests/unit/ — unit tests for individual modules
  • tests/integration/ — integration tests for MCP server functionality
    • tests/integration/suite.ts — main integration test suite where all test cases should be added
    • Other files in this directory set up different transport modes (stdio, SSE, streamable-http) that all use suite.ts
  • tests/helpers.ts — shared test utilities
  • tests/const.ts — test constants

Live probing with mcpc

mcpc (@apify/mcpc) provides a CLI feedback loop against the local server.

Setup

npm install -g @apify/mcpc
npm run build
mcpc --config .mcp.json stdio connect @stdio
mcpc @stdio tools-list   # verify

Usage

Arguments use key:=value syntax (auto-parses as JSON):

mcpc @stdio tools-list
mcpc @stdio tools-call search-actors keywords:="web scraper" limit:=5
mcpc --json @stdio tools-call search-actors keywords:="scraper" | jq ‘.content[0].text’

Key behaviors to verify:

  • search-actors — test valid keywords, empty keywords
  • fetch-actor-details — test valid Actor, non-existent Actor
  • call-actor — test with valid input; check async mode
  • get-actor-output — test field filtering with dot notation, non-existent dataset
  • search-apify-docs / fetch-apify-docs — test relevant and non-existent queries

Testing with MCPJam (optional)

Run MCPJam with npx @mcpjam/inspector@latest.

  1. Click "Add new server", enter URL http://localhost:3001/mcp?ui=true, select "No authentication"
  2. App Builder — select a tool, fill arguments, execute, view rendered widget
  3. Chat — add an OpenAI/Anthropic/OpenRouter API key to chat with widget rendering inline

Testing with ChatGPT (optional)

Test widget rendering on chatgpt.com by exposing the local server via ngrok. See the Apify ChatGPT integration docs for background.

The ngrok credentials are in 1Password. The static domain mcp-apify.ngrok.dev is already set up — add to ~/.config/ngrok/ngrok.yml:

tunnels:
  app:
    addr: 3001
    proto: http
    domain: mcp-apify.ngrok.dev

Then start the tunnel:

ngrok start app

The MCP server API will be reachable at https://mcp-apify.ngrok.dev/?ui=true.

Adding the server in ChatGPT

  1. Go to chatgpt.com and open Settings → Connectors
  2. Click "Add a custom connector"
  3. Enter the URL: https://mcp-apify.ngrok.dev/?ui=true
  4. Save and start a new chat

Important: After restarting ngrok, use the Refresh button in the connector settings to reconnect — ChatGPT does not detect the tunnel restart automatically.