Chatbot Assistant with Gemini + gRPC MCP Server

@omjamnekar

Repository

Basic information

About Chatbot Assistant with Gemini + gRPC MCP Server

grcp chatmodel with mcp server model integrated with gemini model 5o-flash

#chatbot #grpc #gemini

Chatbot Assistant with Gemini + gRPC MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "grcp-chatmodel": {
      "command": "python",
      "args": [
        "server.py"
      ]
    }
  }
}

Chatbot Assistant with Gemini + gRPC MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Chatbot Assistant with Gemini + gRPC?

A simple chatbot backend powered by Google's Gemini API that uses gRPC for client‑server communication. It maintains conversation context per session and follows a clean modular design (client, server, context manager, inference engine). Designed for developers building interactive conversational experiences with gRPC and Gemini.

How to use Chatbot Assistant with Gemini + gRPC?

Install dependencies: pip install -r requirements.txt.
Create a .env file with your GEMINI_API key.
Start the server: python server.py.
Run the client: python client.py. Type messages and receive AI replies; type exit to stop.

Key features of Chatbot Assistant with Gemini + gRPC

gRPC‑based communication between client and server
Integration with Google Gemini API for AI replies
Session‑based context management per chat
Modular code (client, server, context manager, inference engine)
Clean project structure with .proto definitions

Use cases of Chatbot Assistant with Gemini + gRPC

Building a prototype chatbot with stateful conversation history
Learning how to combine gRPC with a modern LLM API
Experimenting with session‑aware AI assistants in Python
Demonstrating a full‑stack gRPC application with Gemini backend

Frequently asked questions

How do I set up the Gemini API key?

Create a `.env` file in the project root and add `GEMINI_API=your_api_key_here`.

What Python version is required?

Python 3.10 or higher is recommended.

Do I need to compile the .proto files myself?

No – the repository includes pre‑generated files (`assistant_pb2.py` and `assistant_pb2_grpc.py`). Ensure they match your `.proto` definitions if you modify them.

How is chat context managed?

The `context_manager.py` module maintains context per session, so the conversation history is preserved for each client.

What transport and authentication does the server use?

Communication uses gRPC. The Gemini API is authenticated via the `GEMINI_API` key stored in the `.env` file.

Comments

More AI & Agents MCP servers

PDI Bridge for Claude

jxrlabs

Connect Claude Desktop to your ServiceNow PDI. Query records, inspect Business Rules, review ACLs, and troubleshoot against your live instance.

strac-mcp-dlp

strac-io

Detect and redact PII, PHI, PCI and secrets — SSNs, credit cards, passports, API keys and cloud credentials — in text and in local files, using the Strac DLP API. Five tools: redact_text, detect_sensitive_data, detect_fi

WordPress MCP Server

miniOrange

miniOrange WordPress MCP Server turns a WordPress site into a Model Context Protocol server, so AI clients like Claude, ChatGPT, Cursor, Gemini and Windsurf can read and edit the site through chat instead of the admin da

Agent Traffic Lab

ATL doesn’t just help agents find tools — it gets the job done. It finds and chooses the right AI provider, tool, or MCP server, executes the task, and switches to a fallback when a provider fails. It can optimize for lo

Legion MCP

faulkj

MCP-native LLM councils for debates, juries, blind panels, voting, refinement, and custom multi-model deliberation.