Tiny-OAI-MCP-Agent

Name: Tiny-OAI-MCP-Agent
Availability: InStock
Author: Warren Zhan

A MCP protocol agent that operates a SQLite using natural language by OpenAI-Compatible LLM.

GitHub

GitHub Stars

User Rating

Not Rated

Favorites

Views

Forks

Issues

Installation

Difficulty

Intermediate

Estimated Time

10-20 minutes

Installation

Add .env to the project according to .env.template and fill in your OPENAI service provider information. Please refer to the format of the service providers I have tested below: ✅ [OpenRouter](https://openrouter.ai/) + [qwen-turbo](https://openrouter.ai/qwen/qwen-turbo)


OPENAI_API_KEY='sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
OPENAI_API_BASE='https://openrouter.ai/api/v1'
LLM_MODEL='qwen/qwen-turbo'

✅ [OpenRouter](https://openrouter.ai/) + [qwen-max](https://openrouter.ai/qwen/qwen-max)


OPENAI_API_KEY='sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
OPENAI_API_BASE='https://openrouter.ai/api/v1'
LLM_MODEL='qwen/qwen-max'

✅ [OpenRouter](https://openrouter.ai/) + [gpt-4o-mini](https://openrouter.ai/openai/gpt-4o-mini)


OPENAI_API_KEY='sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
OPENAI_API_BASE='https://openrouter.ai/api/v1'
LLM_MODEL='openai/gpt-4o-mini'

Additional Resources

GitHub Repository

Author Information

Warren Zhan

@bytedanceChina ShenZhen

GitHub

Followers

Repositories

Gists

Total Contributions

Tags

agent llm mcp mcp-client minimal-example

Related MCPs

AgentChat

AgentChat 是一个基于 LLM 的智能体交流平台，内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作，Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Milvus 和 ElasticSearch 等技术，实现高效的知识检索与工具调用，使用 FastAPI 构建高性能后端服务。

Python

tuui

1063

A desktop MCP client designed as a tool unitary utility integration, accelerating AI adoption through the Model Context Protocol (MCP) and enabling cross-vendor LLM API orchestration.

TypeScript

mcp-client-for-ollama

215

The mcp-client-for-ollama is a simple yet powerful Python client designed for interacting with Model Context Protocol (MCP) servers using Ollama. This client enables local large language models (LLMs) to utilize tools effectively. It primarily facilitates communication with APIs, streamlining workflows and enhancing the capabilities of LLMs.

Python