MCP Remote Server
Enables multi-user remote MCP interactions with Gmail, OpenAI, and Salesforce via a FastMCP server.
README
MCP Remote Server — HTTP-Streamable / Resumable (FastMCP)
A production-style remote MCP (Model Context Protocol) server built with FastMCP, exposing tools over a multi-user, HTTP-streamable (resumable) transport and deployed to the cloud via CI/CD. It integrates Gmail, OpenAI, and Salesforce behind a clean service layer, with JWT-based Salesforce auth.
Part of the SunnyLab build series — the step that took a local MCP server to a resumable, multi-user remote server on cloud. Sanitized public showcase: all secrets, keys, and infra identifiers were removed; configure your own
.env/ CI secrets.
What it demonstrates
- Remote MCP over HTTP-streamable, resumable transport (multi-user, not just local stdio)
- FastMCP server exposing tools/services
- Enterprise integrations — Gmail, OpenAI, Salesforce (JWT bearer flow; keys loaded from env/secret-mounted files at runtime, never committed)
- Cloud-native delivery — Docker, Cloud Build, GitHub Actions (all secrets via
${{ secrets.* }}; project/VM are placeholders) - Log retention cron + a lightweight dashboard
Architecture
MCP clients (multi-user)
│ HTTP-streamable / resumable MCP
▼
FastMCP remote server
├─ tools (gmail / openai / salesforce)
└─ service layer ──► Gmail · OpenAI · Salesforce (JWT)
│
▼
deployed on cloud VM (Docker), CI/CD via GitHub Actions
See mcp_server/ for tools and services.
Tech stack
Python · MCP / FastMCP · HTTP-streamable resumable transport · Gmail/OpenAI/Salesforce integrations · JWT · Docker · Google Cloud Build · GitHub Actions
Project structure
mcp_server/ # FastMCP server, tools, services, config
generate_token.py# OAuth token helper (no secrets committed)
retention_cron.py# log retention job
dashboard.py # lightweight dashboard
.github/ # CI/CD (secrets via ${{ secrets.* }}, placeholders for project/VM)
Dockerfile · docker-compose.yml · cloudbuild.yaml
.env.example # required env vars (no real keys)
Setup
cp .env.example .env # your own keys; SF JWT key path, OPENAI, Google …
pip install -r requirements.txt
# run the FastMCP server (see mcp_server/)
Note
Public portfolio showcase. Credential files (github-deploy-key.json, SF private key, tokens), .env, and infrastructure identifiers were removed before publishing. The code loads all secrets from environment / mounted files at runtime — none are committed.
SunnyLab — building agentic AI in public · Medium @sunnylabtv · YouTube @sunnylabtv
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.