mcp-remote-suite
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-remote-suitelist all files in the workspace"
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 Remote Suite
A production-ready Model Context Protocol (MCP) suite over Streamable HTTP, demonstrating remote connectivity, a filesystem roots security boundary, and where sampling (server-initiated LLM requests) fits in.
It ships three runnable apps on top of one transport layer:
Component | Role | Default URL |
Server ( | FastMCP file server: tools, resources, prompts, sandboxed to a workspace root |
|
GUI client ( | Manual Gradio UI to discover/run tools, read resources, render prompts |
|
AI host ( | Gradio chat where an OpenAI model drives the MCP tools |
|
Architecture
src/mcp_remote_suite/
├── config.py # env-driven settings (pydantic-settings)
├── logging_config.py # shared logging setup
├── _content.py # unwrap MCP result objects -> text/dicts
├── server/
│ ├── security.py # WorkspaceGuard — path-traversal protection
│ ├── files.py # WorkspaceFiles — testable file operations
│ ├── app.py # FastMCP factory + entry point
│ └── __main__.py # python -m mcp_remote_suite.server
├── client/
│ └── base.py # MCPHTTPClient — transport only, no UI/LLM deps
└── apps/
├── gui.py # MCPHTTPClientApp (manual)
└── host.py # MCPHTTPHostApp (LLM-driven, multi-round tool loop)
tests/ # pytest unit tests for the security boundary + filesLayering: apps depend on client and _content; server depends on
security + files. Nothing hardcodes ports/URLs/models — all configuration
flows from config.py.
Related MCP server: TianGong-AI-MCP
Setup
python -m venv .venv
# Windows: .venv\Scripts\activate | Unix: source .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env # optional — adjust ports, model, OpenAI credsRun
Start the server, then either app (each in its own terminal):
mcp-server # http://127.0.0.1:8000
mcp-gui # http://127.0.0.1:7861 (manual)
mcp-host # http://127.0.0.1:7862 (AI chat; needs OpenAI creds)Equivalent module form: python -m mcp_remote_suite.server, etc.
Configuration
All settings come from environment variables or .env (see .env.example).
Key ones: SERVER_HOST/SERVER_PORT, WORKSPACE_DIR, SERVER_URL,
GUI_PORT/HOST_PORT, LOG_LEVEL, and OPENAI_API_KEY / OPENAI_BASE_URL /
OPENAI_MODEL for the AI host.
Security boundary
Every server-side path goes through WorkspaceGuard.resolve(), which resolves
the path and rejects anything escaping the workspace root (.., absolute paths,
symlink escapes) with AccessDeniedError. This is the "roots" enforcement that
makes the server safe to expose remotely.
Tests
pytestThis server cannot be installed
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