I'll help you set up Git tracking for this folder. Let me first check the current state of the directory.

The folder is currently empty and not a Git repository. You have two options:

Option 1: Clone directly into this folder

If you want to replace this folder with the remote repo contents:

Option 2: Initialize and connect to remote (recommended)

This keeps the current folder and pulls into it:

What's the remote repository URL? For example:

• git@github.com:username/ai-receptionist.git (SSH)
• https://github.com/username/ai-receptionist.git (HTTPS)

Once you provide the URL, I can run the commands for you.

I'll set up the folder to track that remote repository and pull it in.

Done! The repository is now set up and tracking the remote. Let me show you what was pulled:

Setup complete! The folder now tracks https://github.com/surijatin/eleven

Summary:

• Initialized Git repository
• Added remote origin
• Pulled and checked out main branch
• Contents: .gitignore and PLAN.md

Useful commands going forward:

• git pull - Fetch and merge latest changes from remote
• git status - Check current state
• git log --oneline - View commit history

This is a plan to build an AI receptionist system that connects ElevenLabs conversational AI to a real phone number, with an EHR integration layer.

Overview

Goal: Build a monorepo connecting:

• ElevenLabs Agent → handles phone conversations
• Twilio → provides the phone number
• InSync EHR MCP Server → patient lookup & appointment booking

Architecture Flow

Phone Call (Twilio) → ElevenLabs Agent → Webhooks → agent-server → Mock FHIR Data
                                                    ↑
Claude/MCP clients → insync-mcp ───────────────────┘

Key Components

The 4 Core Tools

1. searchPatient - Find patients by name/DOB/phone/MRN
2. getPatient - Get patient by ID
3. searchAppointments - Find appointments by patient/date/status
4. createAppointment - Book new appointments

Implementation Phases

1. Scaffold - Set up npm workspaces, TypeScript
2. Shared - FHIR types + mock data (5 patients, 8 appointments)
3. MCP Server - Build the 4 tools for Claude/local dev
4. Agent Server - Express API for ElevenLabs webhooks
5. Wire Up - Connect Twilio, ngrok, configure ElevenLabs dashboard

Want me to start implementing this? I can begin with Phase 1 (scaffolding the monorepo).

Let me fetch the ElevenLabs agents documentation to understand the architecture better.

Let me also fetch the Tools documentation since that's key for MCP integration:

Now I have a clear picture. Here's the simplified architecture for your use case:

Simplified Architecture

Your Goal: ElevenLabs Agent ↔ MCP Server ↔ FHIR/InSync (stubbed)

┌─────────────────────┐      SSE/HTTP       ┌────────────────────┐
│  ElevenLabs Agent   │ ◄──────────────────► │   MCP Server       │
│  (via Widget)       │                      │   (insync-mcp)     │
└─────────────────────┘                      └─────────┬──────────┘
                                                       │
                                                       ▼
                                             ┌────────────────────┐
                                             │  Mock FHIR Data    │
                                             │  (shared package)  │
                                             └────────────────────┘

Key Concepts

1. ElevenLabs MCP Integration

ElevenLabs supports MCP servers via SSE (Server-Sent Events) transport. You register your MCP server URL with ElevenLabs:

Then attach it to your agent via mcp_server_ids in the agent config.

2. Widget vs Phone

The widget is just a UI for the agent:

No Twilio needed — users interact through the browser widget.

3. MCP Server Requirements

Your MCP server needs to:

• Expose an SSE endpoint (ElevenLabs connects to it)
• Implement the MCP protocol (@modelcontextprotocol/sdk)
• Provide tools like searchPatient, getPatient, searchAppointments, createAppointment

Revised Implementation (Minimal)

What You'll Build

1. packages/shared — FHIR types + mock patients/appointments
2. packages/insync-mcp — MCP server (SSE transport) with 4 tools:
   • search_patient
   • get_patient
   • search_appointments
   • create_appointment
3. Hosting — Deploy MCP server to a public URL (ngrok for dev, cloud for prod)
4. ElevenLabs Dashboard — Register MCP server, attach to agent, use widget

Questions Before We Build

1. Do you want to use the existing agent ID (agent_4601kjh11d35f6b8qxymt1kpnyqm) or create a new one?
2. Hosting preference — ngrok for local dev, or deploy somewhere (Vercel, Railway, Fly.io)?
3. Any specific FHIR fields you need beyond patient name, DOB, phone, appointments?

Now I have all the info needed. Let me implement the simplified architecture using Python/uv.

I'll use nx for monorepo management. Let me set up the project structure with nx and uv

Good question! Here's the comparison:

uv vs nx

Here's a comparison of uv vs Nx:

uv vs Nx Comparison

When to Use Each

Use uv when:

• ✅ Python-only project(s)
• ✅ Simple workspace with shared dependencies
• ✅ Need fast dependency resolution (10-100x faster than pip)
• ✅ Publishing Python packages to PyPI
• ✅ Your case: MCP server in Python with shared data package

Use Nx when:

• ✅ JavaScript/TypeScript monorepo
• ✅ Complex task pipelines (build → test → lint → deploy)
• ✅ Need distributed CI/CD with remote caching
• ✅ Multiple frameworks (React, Angular, Node, Next.js)
• ✅ Module boundary enforcement between packages

For Your Project

Use uv — here's why:

1. Python-only — MCP server, shared types, all Python
2. Simple workspace — Just 2 packages (shared + insync-mcp)
3. No complex build pipeline — Just run the MCP server
4. FastMCP uses uv — The MCP Python SDK docs use uv run

Structure:

eleven/
├── pyproject.toml          # Root workspace
├── packages/
│   ├── shared/             # FHIR types + mock data
│   │   └── pyproject.toml
│   └── insync-mcp/         # MCP server
│       └── pyproject.toml

If you later need a frontend (React widget customization) or more complex task orchestration, you could add Nx alongside uv — but for now, uv is the right choice.

Shall I proceed with the uv-based implementation?

Continuing with the uv-based implementation.

Here's how to test the MCP server:

1. Test with MCP Inspector (Interactive UI)

This opens the MCP Inspector UI where you can:

• See all 4 tools registered
• Test tool calls interactively
• View request/response JSON

2. Test with SSE Transport (for ElevenLabs)

The server will be at http://localhost:8000/sse

For ElevenLabs, you'll need a public URL:

Then use the ngrok URL in ElevenLabs dashboard.

3. Test Tools via Python

4. Connect to ElevenLabs

1. Start MCP server with SSE: uv run insync-mcp --transport sse --port 8000
2. Expose with ngrok: ngrok http 8000
3. Go to ElevenLabs Dashboard → Agents → Your Agent → MCP Servers
4. Add MCP server with the ngrok URL: https://xxx.ngrok.io/sse
5. Test via the widget

Project Structure

eleven-insync/
├── pyproject.toml              # uv workspace root
├── README.md
└── packages/
    ├── shared/                 # FHIR types + mock data
    │   ├── pyproject.toml
    │   └── src/insync_shared/
    │       ├── __init__.py
    │       ├── fhir_types.py
    │       ├── mock_patients.py    # 5 patients
    │       └── mock_appointments.py # 8 appointments
    └── insync-mcp/             # MCP server
        ├── pyproject.toml
        └── src/insync_mcp/
            └── __init__.py     # FastMCP server with 4 tools

Done! Let me know if you run into any issues or want to extend the implementation.

The issue is likely the SSE endpoint path. Let me check the correct configuration:

The issue is the SSE endpoint path. By default, FastMCP's SSE endpoint is at /sse, not the root. Try:

1. Update your ElevenLabs config to use: https://ialzv-199-247-196-34.a.free.pinggy.link/sse

Or, I can update the server to run at the root path. Let me also check if ElevenLabs supports the newer "streamable-http" transport:

Let me help debug this. First, let's test the SSE endpoint locally:

I see the issue. The MCP server is rejecting requests due to Host header validation. The request is coming through a pinggy tunnel (brybn-199-247-196-34.a.free.pinggy.link) but the server is configured to only accept localhost.

Let me fix this by updating the MCP server to accept external hosts:

Now I understand! The MCP SDK has DNS rebinding protection enabled by default, only allowing localhost and 127.0.0.1. I need to configure it to allow the tunnel host.

Let me update the MCP server:

Now restart the server. You can do this in your tmux session:

Or I can restart it for you:

Fixed! The server is now accepting requests from the tunnel:

INFO:     199.247.196.34:0 - "GET /sse HTTP/1.1" 200 OK

The fix: Disabled DNS rebinding protection in the MCP server by adding:

This allows external hosts (like your pinggy tunnel or ElevenLabs) to connect. The SSE connection is now working — try connecting from ElevenLabs again.

The patient with MRN MRN004567 is inactive — I added a filter that skips inactive patients. Let me check:

The patient MRN004567 (Robert James Williams) has active=False, and my search_patients function filters out inactive patients. Let me fix that — for an AI receptionist, you'd want to find all patients:

Now restart the server to pick up the changes:

Fixed! Now searching for MRN004567 returns Robert James Williams.

The issue: I had hardcoded active=False patients to be filtered out. Changed it so:

• By default, all patients are returned (active and inactive)
• Optional active_only=True parameter to filter if needed

Try the search again from ElevenLabs — it should work now.

Here are your best free options for hosting the MCP server:

Comparison

Best Choice: Fly.io

Free tier includes:

• 3 shared-cpu-1x VMs
• 3GB persistent volume
• 160GB outbound data/month
• Persistent (no spin-down) — perfect for SSE

Deploy to Fly.io

1. Install flyctl:

2. Login:

3. Create fly.toml in project root:

4. Create Dockerfile:

5. Deploy:

Want me to set up the deployment files for Fly.io? Or would you prefer Railway (simpler but limited credits) or Oracle (more setup but always free)?

Cloudflare Workers — Best Free Option for MCP

Cloudflare is the industry leader for remote MCP servers. They announced the first remote MCP server hosting solution in 2025.

Free Tier Limits

Why Cloudflare for MCP?

1. One-click deploy — Template for MCP servers
2. No credit card required — Truly free tier
3. Edge network — Fast global response times
4. Native MCP support — Official SDK integration
5. Streamable HTTP transport — Current MCP spec standard

Deploy Options

Option 1: Dashboard (Easiest)

Click to deploy: Cloudflare MCP Template

Option 2: CLI

Your Server Architecture

Problem: Your current Python MCP server needs to be converted to TypeScript/JavaScript for Cloudflare Workers.

Options:

Recommendation

If you want truly free + no credit card:

• Port the MCP server to TypeScript and deploy to Cloudflare Workers

If you want to keep Python:

• Use Fly.io (requires credit card but free tier is generous: 3 VMs)

Want me to:

1. Port the server to TypeScript for Cloudflare Workers?
2. Set up Fly.io deployment for the existing Python server?

Modal — Great Option for Python MCP Servers!

Modal is perfect for your use case. One of the testimonials even says:

"We rely on it to handle massive spikes in volume for evals, RL environments, and MCP servers."

Free Tier (Starter Plan)

Pricing (Pay per second)

Example: Running a small MCP server (1 core, 1GB RAM) for 100 hours/month:

• CPU: 100 hrs × $0.047 = $4.70
• Memory: 100 hrs × 1GB × $0.008 = $0.80
• Total: ~$5.50/month (well within $30 free credit)

Why Modal for MCP?

How to Deploy

Modal supports ASGI apps (which the MCP SSE server uses). Here's how:

Deploy:

Recommendation

Modal is your best bet — keep the Python code, deploy with minimal changes, stay within free tier.

Want me to create the Modal deployment files?

Now I have the Modal example. Let me create the deployment files:

Done! I've created the Modal deployment file. Here's what you need to do:

Deploy to Modal (Free $30/month)

1. Install Modal

2. Login

This opens a browser to authenticate.

3. Serve locally (with hot reload)

This gives you a URL like: https://your-workspace--insync-mcp-web.modal.run/mcp/

4. Test with MCP Inspector

• Enter URL: https://your-workspace--insync-mcp-web.modal.run/mcp/
• Transport Type: Streamable HTTP
• Click Connect → List Tools

5. Deploy to production

Key Changes from Local Version

Connecting to ElevenLabs

1. Deploy: modal deploy insync_mcp_modal.py
2. Copy the URL from output
3. In ElevenLabs Dashboard → Agents → MCP Servers
4. Add server with URL ending in /mcp/
5. Select Streamable HTTP transport
6. Test via widget

Want me to help you test the deployment?