MCP Multi-Agent Server
Allows interacting with Gmail to manage emails and perform email-related tasks.
Provides tools for managing CRM data, such as contacts, leads, and opportunities, via Salesforce API.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Multi-Agent ServerSend a follow-up email to client ABC Corp"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Multi-Agent Server
A multi-domain multi-agent system served over MCP (Model Context Protocol) with FastMCP. A set of specialized domain agents (email, CRM, calendar, customer support, helpdesk, reporting) are exposed as MCP tools and coordinated to automate business workflows, with a bilingual Streamlit dashboard for observability.
Part of the SunnyLab build series. Sanitized public showcase — credentials and infrastructure identifiers removed; configure your own
.env.
What it demonstrates
Multi-agent architecture over MCP / FastMCP — domain agents as composable, policy-routed tools
Enterprise integrations — Gmail, Salesforce, and other services behind a service layer
Streamlit dashboard (Korean / English) for runs and observability
Cloud-native delivery — Docker, docker-compose, Cloud Build, GitHub Actions (project/VM values are placeholders)
Architecture
MCP client (Claude Desktop / Cursor / custom)
│ MCP
▼
FastMCP server ── routes ──► domain agents
├─ email ├─ crm ├─ calendar
├─ cs ├─ helpdesk └─ report
│
▼
service layer (Gmail / Salesforce / …) → Streamlit dashboard (KR/EN)See mcp_server/ for agents, tools, and services.
Tech stack
Python · MCP / FastMCP · Salesforce & Gmail integrations · Streamlit · Docker / docker-compose · Google Cloud Build · GitHub Actions
Project structure
mcp_server/ # agents, tools, services (MCP/FastMCP)
dashboard.py # Streamlit dashboard (KR)
dashboard_en.py # Streamlit dashboard (EN)
tests/ # unit tests
cloudbuild.yaml # Cloud Build (placeholders)
docker-compose.yml · Dockerfile
.env.example # required env vars (no real keys)Setup
cp .env.example .env # fill in your own keys (OPENAI/Google/Salesforce …)
pip install -r requirements.txt
# run the MCP server (see mcp_server/) and the dashboard:
streamlit run dashboard.pyNote
Public portfolio showcase. Credential files, tokens, and infra identifiers (GCP project, VM IP) were removed before publishing; CI/deploy files use placeholders and require your own configuration.
SunnyLab — building agentic AI in public · Medium @sunnylabtv · YouTube @sunnylabtv
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