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Set Up and Deploy AI Receptionist MCP ServerPublic

The user wanted to set up a folder to track and pull from a remote GitHub repository, then understand and implement an AI receptionist system architecture using ElevenLabs agents and an MCP server. After setting up the repository and clarifying the architecture, they built and tested the MCP server, resolved connection and data filtering issues, and finally explored free hosting options for deployment including Cloudflare Workers and Modal. The user received guidance on testing and deploying the server with free cloud providers.

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Mar 1, 6:10 PM

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

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:

bash

Option 2: Initialize and connect to remote (recommended)

This keeps the current folder and pulls into it:

bash

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.

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

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

read @PLAN.md and tlel me

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

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

Implementation Phases

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

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

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

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:

bash

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

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

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

Questions Before We Build

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

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

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

what is the difference between them?

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

no search the net

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

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:

Python-only — MCP server, shared types, all Python
Simple workspace — Just 2 packages (shared + insync-mcp)
No complex build pipeline — Just run the MCP server
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?

ok i guess for now we can continue.

Continuing with the uv-based implementation.

i can test myself. tell me what to do

Here's how to test the MCP server:

1. Test with MCP Inspector (Interactive UI)

bash

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

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

For ElevenLabs, you'll need a public URL:

bash

Then use the ngrok URL in ElevenLabs dashboard.

3. Test Tools via Python

bash

4. Connect to ElevenLabs

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

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.

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:

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:

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.

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

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

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:

bash

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:

python

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

{
"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

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.

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

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

2. Login:

bash

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)?

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

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?

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

Deploy Options

Option 1: Dashboard (Easiest)

Click to deploy: Cloudflare MCP Template

Option 2: CLI

bash

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:

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

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

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

Deploy:

bash

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?

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

see this and add

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

bash

2. Login

bash

This opens a browser to authenticate.

3. Serve locally (with hot reload)

bash

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

4. Test with MCP Inspector

bash

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

5. Deploy to production

bash

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

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

Want me to help you test the deployment?

Option 1: Clone directly into this folder

Option 2: Initialize and connect to remote (recommended)

Overview

Architecture Flow

Key Components

The 4 Core Tools

Implementation Phases

Simplified Architecture

Key Concepts

1. ElevenLabs MCP Integration

2. Widget vs Phone

3. MCP Server Requirements

Revised Implementation (Minimal)

What You'll Build

Questions Before We Build

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

Implement InSync MCP Server for ElevenLabs

Goal

Architecture

Checklist

Phase 1: Project Scaffold

Phase 2: Shared Package

Phase 3: MCP Server

Phase 4: Testing

Instructions

uv vs nx

uv vs Nx Comparison

When to Use Each

Use uv when:

Use Nx when:

For Your Project

1. Test with MCP Inspector (Interactive UI)

2. Test with SSE Transport (for ElevenLabs)

3. Test Tools via Python

4. Connect to ElevenLabs

Project Structure

Comparison

Best Choice: Fly.io

Deploy to Fly.io

Cloudflare Workers — Best Free Option for MCP

Free Tier Limits

Why Cloudflare for MCP?

Deploy Options

Option 1: Dashboard (Easiest)

Option 2: CLI

Your Server Architecture

Recommendation

Modal — Great Option for Python MCP Servers!

Free Tier (Starter Plan)

Pricing (Pay per second)

Why Modal for MCP?

How to Deploy

Recommendation

Deploy to Modal (Free $30/month)

1. Install Modal

2. Login

3. Serve locally (with hot reload)

4. Test with MCP Inspector

5. Deploy to production

Key Changes from Local Version

Connecting to ElevenLabs