# Trace: Set Up and Deploy AI Receptionist MCP Server

Agent: pi | Model: Gemini 3 Flash Preview | Project: ai-receptionist

---

## User

How do I set up this folder to track a remote github repository and then pull that in

## Assistant

## Assistant

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

## Assistant

## Assistant

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:
```bash
cd .. && rm -rf ai-receptionist && git clone <repo-url> ai-receptionist
```

## Option 2: Initialize and connect to remote (recommended)
This keeps the current folder and pulls into it:

```bash
git init
git remote add origin <repo-url>
git fetch origin
git checkout -b main origin/main  # or whatever the default branch is
```

**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.

## User

https://github.com/surijatin/eleven use

## Assistant

## Assistant

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

## Assistant

## Assistant

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

## Assistant

## Assistant

**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

## User

read @PLAN.md and tlel me

## Assistant

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## Assistant

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

| Package | Purpose |
|---------|---------|
| `shared` | Types, config, mock FHIR patient/appointment data |
| `insync-mcp` | MCP server with 4 tools (search/get patients, search/create appointments) |
| `agent-server` | Express server for ElevenLabs webhooks, phone import, outbound calls |

## 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).

## User

Hold off on implementaion. Help me understand the architecture first. We can skip the real phone number integration and use the widgets that ElevenLabs provides. The first goal is to be able host an MCP server with custom tools that can interact with FHIR/InSync (stub this for now, this will get proven properly later). 

https://elevenlabs.io/docs/eleven-agents/overview

## Assistant

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

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

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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:

```bash
# Create MCP server in ElevenLabs
POST /v1/convai/mcp-servers
{
  "config": {
    "url": "https://your-server.com/mcp",  # Your hosted MCP server
    "name": "insync-mcp",
    "transport": "SSE"
  }
}
```

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:
```html
<elevenlabs-convai agent-id="YOUR_AGENT_ID"></elevenlabs-convai>
<script src="https://unpkg.com/@elevenlabs/convai-widget-embed" async></script>
```

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)

| Package | Purpose | Needed? |
|---------|---------|---------|
| `shared` | Types + mock FHIR data | ✅ Yes |
| `insync-mcp` | MCP server with 4 tools | ✅ Yes |
| `agent-server` | Express for webhooks | ❌ **Skip** (use MCP instead) |
| Twilio/Phone | Real phone calls | ❌ **Skip** (use widget) |

---

## 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?

## User

ok continue. this looks fine. use uv if possible for the insync mcp. search web for latest

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

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## User

───────────────────────────────────────────────────────────────────────
🔄 RALPH LOOP: insync-mcp-implementation | Iteration 1/50
───────────────────────────────────────────────────────────────────────

## Current Task (from .ralph/insync-mcp-implementation.md)

# Implement InSync MCP Server for ElevenLabs

## Goal
Build a Python MCP server (using uv) that provides FHIR patient/appointment tools for ElevenLabs agents.

## Architecture
```
ElevenLabs Widget → ElevenLabs Agent → MCP Server (SSE) → Mock FHIR Data
```

## Checklist

### Phase 1: Project Scaffold
- [ ] Create root pyproject.toml with uv workspace
- [ ] Create packages/shared with FHIR types and mock data
- [ ] Create packages/insync-mcp structure

### Phase 2: Shared Package
- [ ] Define FHIR types (FhirPatient, FhirAppointment, FhirBundle)
- [ ] Create 5 mock patients with US Core profiles
- [ ] Create 8 mock appointments referencing patients

### Phase 3: MCP Server
- [ ] Set up FastMCP server with SSE transport
- [ ] Implement search_patient tool
- [ ] Implement get_patient tool
- [ ] Implement search_appointments tool
- [ ] Implement create_appointment tool

### Phase 4: Testing
- [ ] Test with MCP Inspector (`uv run mcp dev`)
- [ ] Verify SSE transport works
- [ ] Document how to connect to ElevenLabs


---

## Instructions

User controls: ESC pauses the assistant. Send a message to resume. Run /ralph-stop when idle to stop the loop.

You are in a Ralph loop (iteration 1 of 50).

**THIS ITERATION: Process approximately 3 items, then call ralph_done.**

1. Work on the next ~3 items from your checklist
2. Update the task file (.ralph/insync-mcp-implementation.md) with your progress
3. When FULLY COMPLETE, respond with: <promise>COMPLETE</promise>
4. Otherwise, call the ralph_done tool to proceed to next iteration


use nx for monorepo management so that its easy to extend later

## Assistant

## Assistant

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

## User

what is the difference between them?

## Assistant

## Assistant

Good question! Here's the comparison:

## uv vs nx

| Feature | uv | nx |
|---------|-----|-----|
| **Purpose** | Python package/project manager | Monorepo build system |
| **Languages** | Python only | JS/TS, Python, Go, Java, etc. |
| **Workspace support** | Basic (via `uv worktree`) | Full-featured |
| **Task orchestration** | ❌ No | ✅ Parallel, cached, dependency-aware |
| **Build caching** |

## User

no search the net

## Assistant

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## User

use websearch and tell me about nx vs uv and when to use

## Assistant

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Here's a comparison of **uv** vs **Nx**:

---

## uv vs Nx Comparison

| Aspect | uv | Nx |
|--------|----|----|
| **Purpose** | Python package manager & project manager | JavaScript/TypeScript monorepo build system |
| **Ecosystem** | Python only | JS/TS, but also supports Java, .NET, Gradle, Maven |
| **Workspaces** | ✅ Simple workspaces (Cargo-inspired) | ✅ Full monorepo management |
| **Caching** | Per-project lockfiles, dependency caching | Advanced task caching (local + remote) |
| **Task Running** | `uv run` for commands/scripts | `nx run`, `nx affected`, task pipelines |
| **Language** | Rust (extremely fast) | TypeScript |

---

## 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?

## User

ok i guess for now we can continue.

## Assistant

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Continuing with the uv-based implementation.

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## User

i can test myself. tell me what to do

## Assistant

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Here's how to test the MCP server:

## 1. Test with MCP Inspector (Interactive UI)

```bash
uv run mcp dev packages/insync-mcp/src/insync_mcp/__init__.py
```

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)

```bash
# Start server on port 8000
uv run insync-mcp --transport sse --port 8000
```

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

For ElevenLabs, you'll need a public URL:
```bash
# In another terminal
ngrok http 8000
```

Then use the ngrok URL in ElevenLabs dashboard.

---

## 3. Test Tools via Python

```bash
uv run python -c "
from insync_shared import search_patients, MOCK_PATIENTS

# List all mock patients
print('=== Mock Patients ===')
for p in MOCK_PATIENTS:
    print(f'{p.id}: {p.get_display_name()} - {p.birthDate}')

# Search by family name
print('\n=== Search: family=Rodriguez ===')
results = search_patients(family='Rodriguez')
for p in results:
    print(p.get_display_name())
"
```

---

## 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
```

## Assistant

## Assistant

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

## User

Internal error: Unexpected ExceptionGroup occurred while connecting to MCP server at https://ialzv-199-247-196-34.a.free.pinggy.link/ using TransportType.SSE transport. Please try again or contact support if the issue persists.

## Assistant

## Assistant

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

## Assistant

## Assistant

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:

## Assistant

## User

Internal error: Unexpected ExceptionGroup occurred while connecting to MCP server at https://ialzv-199-247-196-34.a.free.pinggy.link/sse using TransportType.SSE transport. Please try again or contact support if the issue persists.

## Assistant

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

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## User

you are in a tmux session with the server logs running on window 1. see the request validation failed erorr

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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:

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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:

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

```bash
# In the window running the server, press Ctrl+C to stop it
# Then run:
uv run insync-mcp --transport sse --port 8000
```

Or I can restart it for you:

## Assistant

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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:

```python
from mcp.server.transport_security import TransportSecuritySettings

security_settings = TransportSecuritySettings(
    enable_dns_rebinding_protection=False,
)

mcp = FastMCP(
    name="InSync EHR",
    transport_security=security_settings,
)
```

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

## User

{
  "mrn": "MRN004567"
}
{
  "mrn": "MRN004567"
}
Tool execution time

1.1 s

{
  "content": [
    {
      "type": "text",
      "text": "{\n  \"resourceType\": \"Bundle\",\n  \"type\": \"searchset\",\n  \"total\": 0,\n  \"entry\": []\n}"
    }
  ],
  "structuredContent": {
    "result": "{\n  \"resourceType\": \"Bundle\",\n  \"type\": \"searchset\",\n  \"total\": 0,\n  \"entry\": []\n}"
  },
  "isError": false
}

why do i get this? i can see there is the MRN i pasted in the mock data

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## Assistant

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

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## Assistant

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:

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Now restart the server to pick up the changes:

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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.

## User

ok great. now, how do i deploy it to a static location? what are my free options?

## Assistant

## Assistant

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

## Comparison

| Platform | Free Tier | Pros | Cons |
|----------|-----------|------|------|
| **Fly.io** | 3 VMs, 3GB storage | Best for SSE, persistent, easy CLI | Requires credit card |
| **Railway** | $5 credit/month | Simplest deployment, great DX | Credit runs out fast |
| **Render** | 750 hours/month | No credit card required | **Spins down after 15min inactivity** |
| **Oracle Cloud** | Always-free ARM VM | Never expires, 4 VMs | Complex setup, requires CC |
| **Google Cloud Run** | 2M requests/month | Serverless, scales to zero | Cold starts, SSE tricky |

---

## 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:**
```bash
curl -L https://fly.io/install.sh | sh
```

**2. Login:**
```bash
fly auth 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)?

## User

use tavily web search and find the latest free providers for remotemcp? cloudflare?

## Assistant

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## 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

| Feature | Free Tier | Notes |
|---------|-----------|-------|
| **Requests** | 100,000/day | Resets at midnight UTC |
| **CPU time** | 10ms/request | Enough for simple tools |
| **Memory** | 128 MB | Per isolate |
| **Workers** | 100 | Per account |
| **Duration** | Unlimited | For HTTP requests (while client connected) |
| **Subrequests** | 50/request | External API calls |

---

## 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](https://dash.cloudflare.com/?to=/:account/workers-and-pages/create/deploy-to-workers&template=cloudflare/ai/demos/remote-mcp-authless)

### Option 2: CLI

```bash
# Create MCP server project
npm create cloudflare@latest -- insync-mcp --template=cloudflare/ai/demos/remote-mcp-authless

# Deploy
cd insync-mcp
npx wrangler@latest deploy
```

---

## Your Server Architecture

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

**Options:**

| Option | Pros | Cons |
|--------|------|------|
| **Port to TypeScript** | Native Workers support, fastest | Rewrite required |
| **Use Python Workers (Beta)** | Keep Python code | Beta, limited features |
| **Alternative: Fly.io** | Keep Python, SSE works well | Requires credit card |

---

## 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?

## User

search https://modal.com/ and see if i can use them

## Assistant

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## 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)

| Feature | Free Tier |
|---------|-----------|
| **Monthly credits** | $30/month free |
| **Base cost** | $0/month |
| **Seats** | 3 included |
| **Web endpoints** | 8 deployed endpoints |
| **Containers** | 100 concurrent |
| **GPU concurrency** | 10 |

---

## Pricing (Pay per second)

| Resource | Cost |
|----------|------|
| **CPU** | $0.0000131/core/sec (~$0.047/core-hour) |
| **Memory** | $0.00000222/GiB/sec |
| **Nvidia T4 GPU** | $0.000164/sec (~$0.59/hour) |

**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?

| Feature | Modal |
|---------|-------|
| **Python-native** | Keep your existing code |
| **ASGI support** | FastAPI/Starlette (MCP SSE uses this) |
| **WebSockets** | ✅ Supported |
| **Cold starts** | Sub-second (very fast) |
| **Autoscale to zero** | ✅ No idle costs |
| **No credit card** | Required for signup |

---

## How to Deploy

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

```python
# modal_app.py
import modal

app = modal.App("insync-mcp")

image = modal.Image.from_registry(
    "python:3.11-slim"
).pip_install(
    "mcp[cli]",
    "pydantic",
)

@app.function(image=image, allow_concurrent_inputs=10)
@modal.asgi_app()
def serve():
    from insync_mcp import mcp
    return mcp.sse_app()
```

**Deploy:**
```bash
pip install modal
modal auth login
modal deploy modal_app.py
```

---

## Recommendation

| Platform | Best For |
|----------|----------|
| **Modal** ✅ | Python MCP servers, $30/month free, no rewrite needed |
| **Cloudflare Workers** | TypeScript, 100k requests/day, edge network |
| **Fly.io** | Persistent SSE, requires credit card |

**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?

## User

https://modal.com/docs/examples/mcp_server_stateless 

see this and add

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

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

---

## Deploy to Modal (Free $30/month)

### 1. Install Modal
```bash
pip install modal
```

### 2. Login
```bash
modal auth login
```
This opens a browser to authenticate.

### 3. Serve locally (with hot reload)
```bash
modal serve insync_mcp_modal.py
```
This gives you a URL like: `https://your-workspace--insync-mcp-web.modal.run/mcp/`

### 4. Test with MCP Inspector
```bash
npx @modelcontextprotocol/inspector
```
- Enter URL: `https://your-workspace--insync-mcp-web.modal.run/mcp/`
- Transport Type: **Streamable HTTP**
- Click Connect → List Tools

### 5. Deploy to production
```bash
modal deploy insync_mcp_modal.py
```

---

## Key Changes from Local Version

| Aspect | Local (uv) | Modal |
|--------|------------|-------|
| **Transport** | SSE | Streamable HTTP |
| **Package** | `mcp.server.fastmcp` | `fastmcp` |
| **State** | Stateful | Stateless |
| **URL** | `http://localhost:8000/sse` | `https://xxx.modal.run/mcp/` |

---

## 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?