Ncp

NCP - Natural Context Provider

1 MCP to rule them all

Your MCPs, supercharged. Find any tool instantly, execute with code mode, run on schedule, discover skills, load Photons, ready for any client. Smart loading saves tokens and energy.

Related Portel project: Photon turns TypeScript methods into reliable agent-facing capabilities: MCP tools, embedded app UIs, CLI commands, web routes, schedules, webhooks, retries, and Beam interfaces. NCP helps agents discover and operate tools; Photon helps developers build the tools and apps agents can trust.

💍 What is NCP?

Instead of your AI juggling 50+ tools scattered across different MCPs, NCP gives it a single, unified interface with code mode execution, scheduling, skills discovery, and custom Photons.

Your AI sees just 2-3 simple tools:

• find - Search for any tool, skill, or Photon: "I need to read a file" → finds the right tool automatically
• code - Execute TypeScript directly: await github.create_issue({...}) (code mode, enabled by default)
• run - Execute tools individually (when code mode is disabled)

Behind the scenes, NCP manages all 50+ tools + skills + Photons: routing requests, discovering the right capability, executing code, scheduling tasks, managing health, and caching responses.

Why this matters:

• Your AI stops analyzing "which tool do I use?" and starts doing actual work
• Code mode lets AI write multi-step TypeScript workflows combining tools, skills, and scheduling
• Skills provide domain expertise: canvas design, PDF manipulation, document generation, more
• Photons enable custom TypeScript MCPs without npm publishing
• 97% fewer tokens burned on tool confusion (2,500 vs 103,000 for 80 tools)
• 5x faster responses (sub-second tool selection vs 5-8 seconds)
• Your AI becomes focused. Not desperate.

🚀 NEW: Project-level configuration - each project can define its own MCPs automatically

What's MCP? The Model Context Protocol by Anthropic lets AI assistants connect to external tools and data sources. Think of MCPs as "plugins" that give your AI superpowers like file access, web search, databases, and more.

📑 Quick Navigation

• The Problem - Why too many tools break your AI
• The Solution - How NCP transforms your experience
• Getting Started - Installation & quick start
• Try It Out - See the CLI in action
• Supercharged Features - How NCP empowers your MCPs
• Setup by Client - Claude Desktop, Cursor, VS Code, etc.
• Popular MCPs - Community favorites to add
• Advanced Features - Project config, scheduling, remote MCPs
• Troubleshooting - Common issues & solutions
• How It Works - Technical deep dive
• Contributing - Help us improve NCP

😤 The MCP Paradox: From Assistant to Desperate

You gave your AI assistant 50 tools to be more capable. Instead, you got desperation:

• Paralyzed by choice ("Should I use read_file or get_file_content?")
• Exhausted before starting ("I've spent my context limit analyzing which tool to use")
• Costs explode (50+ tool schemas burn tokens before any real work happens)
• Asks instead of acts (used to be decisive, now constantly asks for clarification)

🧸 Why Too Many Tools Break the System

Think about it like this:

A child with one toy → Treasures it, masters it, creates endless games with it A child with 50 toys → Can't hold them all, gets overwhelmed, stops playing entirely

Your AI is that child. MCPs are the toys. More isn't always better.

The most creative people thrive with constraints, not infinite options. A poet given "write about anything" faces writer's block. Given "write a haiku about rain"? Instant inspiration.

Your AI is the same. Give it one perfect tool → Instant action. Give it 50 tools → Cognitive overload. NCP provides just-in-time tool discovery so your AI gets exactly what it needs, when it needs it.

📊 The Before & After Reality

Before NCP: Desperate Assistant 😵‍💫

When your AI assistant manages 50 tools directly:

🤖 AI Assistant Context:
├── Filesystem MCP (12 tools) ─ 15,000 tokens
├── Database MCP (8 tools) ─── 12,000 tokens
├── Web Search MCP (6 tools) ── 8,000 tokens
├── Email MCP (15 tools) ───── 18,000 tokens
├── Shell MCP (10 tools) ───── 14,000 tokens
├── GitHub MCP (20 tools) ──── 25,000 tokens
└── Slack MCP (9 tools) ────── 11,000 tokens

💀 Total: 80 tools = 103,000 tokens of schemas

What happens:

• AI burns 50%+ of context just understanding what tools exist
• Spends 5-8 seconds analyzing which tool to use
• Often picks wrong tool due to schema confusion
• Hits context limits mid-conversation

After NCP: Executive Assistant ✨

With NCP as Chief of Staff:

🤖 AI Assistant Context:
└── NCP (2 unified tools) ──── 2,500 tokens

🎯 Behind the scenes: NCP manages all 80 tools
📈 Context saved: 100,500 tokens (97% reduction!)
⚡ Decision time: Sub-second tool selection
🎪 AI behavior: Confident, focused, decisive

Real results from our testing:

Translation:

• 5x faster responses (8 seconds → 1.5 seconds)
• 12x longer conversations before hitting limits
• 90% reduction in wrong tool selection
• Zero context exhaustion in typical sessions

📋 Prerequisites

• Node.js 18+ (Download here)
• npm (included with Node.js) or npx for running packages
• Command line access (Terminal on Mac/Linux, Command Prompt/PowerShell on Windows)

🚀 Installation

Choose your MCP client for setup instructions:

Quick Start (npm)

For advanced users or MCP clients not listed above:

Step 1: Install NCP

npm install -g @portel/ncp

Step 2: Import existing MCPs (optional)

ncp config import  # Paste your config JSON when prompted

Step 3: Configure your MCP client

Add to your client's MCP configuration:

{
  "mcpServers": {
    "ncp": {
      "command": "ncp"
    }
  }
}

✅ Done! Your AI now sees just 2 tools instead of 50+.

🧪 Test Drive: See the Difference Yourself

Want to experience what your AI experiences? NCP has a human-friendly CLI:

🔍 Smart Discovery

# Ask like your AI would ask:
ncp find "I need to read a file"
ncp find "help me send an email"
ncp find "search for something online"

Notice: NCP understands intent, not just keywords. Just like your AI needs.

📋 Ecosystem Overview

# See your complete MCP ecosystem:
ncp list --depth 2

# Get help anytime:
ncp --help

⚡ Direct Testing

# Test any tool safely:
ncp run filesystem read_file --path "/tmp/test.txt"

Why this matters: You can debug and test tools directly, just like your AI would use them.

✅ Verify Everything Works

# 1. Check NCP is installed correctly
ncp --version

# 2. Confirm your MCPs are imported
ncp list

# 3. Test tool discovery
ncp find "file"

# 4. Test a simple tool (if you have filesystem MCP)
ncp run filesystem read_file --path "/tmp/test.txt" --dry-run

✅ Success indicators:

• NCP shows version number
• ncp list shows your imported MCPs
• ncp find returns relevant tools
• Your AI client shows only NCP in its tool list

💪 From Tools to Automation: The Real Power

You've seen find (discover tools) and code (execute TypeScript). Individually, they're useful. Together with scheduling, they become an automation powerhouse.

A Real Example: The MCP Conference Scraper

We wanted to stay on top of MCP-related conferences and workshops for an upcoming release. Instead of manually checking websites daily, we asked Claude:

"Set up a daily scraper that finds MCP conferences and saves them to a CSV file"

What Claude did:

1. Used code to write the automation:

   // Search the web for MCP conferences
   const results = await web.search({
     query: "Model Context Protocol conference 2025"
   });
   
   // Read each result and extract details
   for (const url of results) {
     const content = await web.read({ url });
     // Extract title, deadline, description...
     // Save to ~/.ncp/mcp-conferences.csv
   }

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2. Used schedule to automate it:

   ncp schedule create code:run "every day at 9am" \
     --name "MCP Conference Scraper" \
     --catchup-missed

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How to set this up yourself:

First, install the web photon (provides search and read capabilities):

# Install from the official photons repo
ncp photon add https://raw.githubusercontent.com/portel-dev/photons/main/web.photon.ts

Then a

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