personamanagmentlayer__pcl/docs/AI_PLATFORM_INTEGRATION.md

PCL Integration with AI Platforms

Overview

This guide shows how to integrate PCL with various AI platforms and assistants.

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✅ MCP-Compatible Platforms (Native Support)

These platforms support the Model Context Protocol (MCP) natively and can use PCL directly without modification.

1. Claude Desktop ✅

Status: Full native MCP support

Setup:

1. Install PCL SDK:

   npm install @pcl/sdk
   

2. Create MCP Server: (pcl-mcp-server.js)

   import { PclServer, StdioTransport, createRuntime, compile } from '@pcl/sdk';
   import { readFileSync } from 'fs';
   
   const source = readFileSync('./personas.pcl', 'utf-8');
   const compiled = compile(source);
   const runtime = createRuntime();
   runtime.load(compiled.value.program);
   
   const server = new PclServer({
     name: 'pcl-server',
     version: '1.0.0',
     runtime,
   });
   
   const transport = new StdioTransport();
   server.connect(transport);
   

3. Configure Claude Desktop:

   Add to ~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

   {
     "mcpServers": {
       "pcl": {
         "command": "node",
         "args": ["/absolute/path/to/pcl-mcp-server.js"]
       }
     }
   }
   

4. Restart Claude Desktop

5. Use PCL Personas:

   You: List available PCL personas
   
   Claude: [Calls persona/list tool automatically]
   
   You: Execute the Analyst persona to analyze this data
   
   Claude: [Calls persona/execute with Analyst]
   

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2. Claude Code (VS Code Extension) ✅

Status: Full MCP support

Setup:

1. Install Claude Code extension in VS Code

2. Create .claude/mcp.json in your workspace:

   {
     "mcpServers": {
       "pcl": {
         "command": "node",
         "args": ["./pcl-mcp-server.js"],
         "description": "PCL Persona Control Language"
       }
     }
   }
   

3. Claude Code will automatically discover and use PCL tools

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3. Cursor ✅

Status: MCP support available

Setup:

1. Create cursor-mcp.json:

   {
     "servers": {
       "pcl": {
         "command": "node",
         "args": ["./pcl-mcp-server.js"]
       }
     }
   }
   

2. Configure in Cursor settings

3. Use PCL personas in Cursor chat

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4. Zed Editor ✅

Status: MCP support

Setup:

Similar to Claude Code - configure MCP server in Zed settings.

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5. Continue.dev ✅

Status: MCP compatible

Setup:

Configure in Continue.dev extension settings.

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❌ Non-MCP Platforms (Requires REST API Wrapper)

These platforms do not support MCP natively. You need to create a REST API wrapper.

1. ChatGPT (OpenAI) ❌

Status: No MCP support - use REST API + Actions

Setup:

Step 1: Create REST API Server

Use the provided REST API wrapper:

# In examples/
node rest-api-wrapper.ts

This starts an HTTP server at http://localhost:3000 with endpoints:

• GET /api/personas - List personas
• POST /api/personas/:id/execute - Execute persona
• GET /api/teams - List teams
• POST /api/teams/:id/execute - Execute team

Step 2: Deploy REST API

Deploy to a public URL (Render, Railway, Fly.io, etc.):

# Example: Deploy to Render
git push render main

Get your public URL: https://your-pcl-api.onrender.com

Step 3: Create ChatGPT Action

1. Go to ChatGPT → Create a GPT
2. Click "Configure" → "Actions" → "Create new action"
3. Import OpenAPI spec from examples/openapi-spec.yaml
4. Update server URL to your deployed API
5. Save and test

Step 4: Use in ChatGPT

You: List available PCL personas

ChatGPT: [Calls GET /api/personas]

You: Execute the Analyst persona with this data: [...]

ChatGPT: [Calls POST /api/personas/Analyst/execute]

Limitations:

• Requires separate REST API deployment
• Not as seamless as MCP
• Additional latency from HTTP calls
• Need to manage API authentication

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2. DeepSeek ❌

Status: No MCP support - use REST API

Setup:

Same as ChatGPT above. DeepSeek would need to:

1. Access your deployed REST API
2. Use standard HTTP requests
3. Parse JSON responses

Current Status: DeepSeek doesn't have a plugin/action system like ChatGPT, so integration is more limited.

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3. Google Gemini ❌

Status: No MCP support - use REST API + Extensions

Setup:

Similar to ChatGPT - deploy REST API and configure as a Gemini Extension.

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4. Perplexity AI ❌

Status: No MCP support

Limited integration options currently.

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Comparison Matrix

Platform	MCP Support	Integration Method	Difficulty	Latency
Claude Desktop	✅ Native	MCP (stdio)	Easy	Low
Claude Code	✅ Native	MCP (stdio)	Easy	Low
Cursor	✅ Native	MCP (stdio)	Easy	Low
Zed	✅ Native	MCP (stdio)	Easy	Low
Continue.dev	✅ Native	MCP (stdio)	Easy	Low
ChatGPT	❌ None	REST API + Actions	Medium	Medium
DeepSeek	❌ None	REST API (manual)	Hard	Medium
Gemini	❌ None	REST API + Extensions	Medium	Medium
Perplexity	❌ None	Not available	N/A	N/A

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Recommended Approach by Use Case

For Development (Best Experience)

Use: Claude Desktop or Claude Code

• ✅ Native MCP support
• ✅ Lowest latency
• ✅ Best integration
• ✅ No deployment needed

For Production/Public Access

Use: REST API + ChatGPT Actions

• ✅ Wider accessibility
• ✅ Can serve multiple clients
• ⚠️ Requires deployment
• ⚠️ Additional latency

For Team Collaboration

Use: Cursor + MCP

• ✅ IDE integration
• ✅ Team can share MCP config
• ✅ Version control friendly

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Future MCP Support

Platforms that might add MCP support:

• 🔮 ChatGPT (if OpenAI adopts MCP)
• 🔮 DeepSeek (if they implement MCP client)
• 🔮 GitHub Copilot (possible future integration)
• 🔮 Gemini (Google may support MCP)

MCP is an open protocol created by Anthropic, so adoption depends on each platform choosing to implement it.

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Quick Decision Guide

Do you have Claude Desktop?

• YES → Use MCP (easiest!)
• NO → Continue below

Do you use VS Code?

• YES → Use Claude Code extension with MCP
• NO → Continue below

Do you need ChatGPT specifically?

• YES → Deploy REST API + create ChatGPT Action
• NO → Try Cursor or Zed with MCP

Do you want the simplest setup?

• Download Claude Desktop + configure MCP (5 minutes)

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Summary

Best Option: Claude Desktop or Claude Code (native MCP support)

For ChatGPT/DeepSeek: You must create a REST API wrapper and deploy it publicly

Key Takeaway: MCP provides the best PCL integration experience, but you can still use PCL with non-MCP platforms through HTTP APIs.

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Additional Resources

• MCP Integration Guide
• REST API Example
• OpenAPI Specification
• MCP Protocol Spec