About Multi Server Tool Enabled MCP Chat App
Multi Server Tool Enabled MCP Chat App is an MCP server published by MohanBisunke in the AI category: a Streamlit-based chat application powered by LangChain, OpenAI, and Model Context Protocol (MCP). This project demonstrates how a single LLM can seamlessly interact with multiple MCP servers—both local and remote—to reason, call tools, and return concise final answers. It has been installed 0 times through Conduid.
Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.
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npx multi-server-tool-enabled-mcp-chat-appThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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README
🧰 MCP Chat – Multi-Server Tool-Enabled Chat App
A Streamlit-based chat application powered by LangChain, OpenAI, and Model Context Protocol (MCP). This project demonstrates how a single LLM can seamlessly interact with multiple MCP servers—both local and remote—to reason, call tools, and return concise final answers.
✨ Built and tested with FastMCP v2, including validation via MCP Inspector.
✨ Features
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💬 Interactive Chat UI using Streamlit
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🔌 Multi-Server MCP Integration
- Remote FastMCP cloud server
- Local Python-based MCP server
- Node.js MCP server
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🧠 Tool-aware LLM reasoning with automatic tool selection
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🧾 Clean chat history (no tool noise or intermediate steps shown)
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🖥️ Terminal-based MCP chat also implemented (outside the Streamlit UI)
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☁️ FastMCP Cloud Server tested via MCP Inspector
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🔄 Async-safe tool execution with proper message ordering
🏗️ Architecture Overview
Streamlit UI
|
v
LangChain (ChatOpenAI)
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v
MultiServerMCPClient
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+── Remote FastMCP Server (HTTP)
+── Local Python MCP Server (stdio)
+── Node.js MCP Server (stdio)
The LLM:
- Receives user input
- Decides whether tools are required
- Executes tools via MCP servers
- Returns a final concise answer only
🔌 Configured MCP Servers
1. Expense Tracker (Remote – FastMCP Cloud)
- Transport:
streamable_http - Deployed on: FastMCP Cloud
- Tested with: MCP Inspector
https://expense-tracker-app.fastmcp.app/mcp
2. Manim MCP Server (Local – Python)
- Transport:
stdio - Purpose: Manim-based computation / rendering
- Runs locally using a Python executable
3. Sequential Thinking Server (Local – Node.js)
- Transport:
stdio - Powered by:
@modelcontextprotocol/server-sequential-thinking - Used for: Step-by-step structured reasoning
⚙️ Tech Stack
- Python 3.12
- Streamlit
- LangChain
- OpenAI (ChatOpenAI)
- FastMCP v2
- Model Context Protocol (MCP)
- Node.js (for MCP servers)
- dotenv
📌 Notes
- The system prompt enforces concise final answers
- Tool execution status is never narrated to the user
- Easily extensible—just add more MCP servers to the config!
🙌 Acknowledgements
- LangChain
- Model Context Protocol
- FastMCP
- Streamlit
- OpenAI
README mirrored from the source repository 3 months ago. The original is authoritative.