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MCP-Ollama Client

@Nagharjun17

代码仓库

基本信息

分类

AI 与智能体

许可证

MIT license

传输方式

stdio

作者

Nagharjun17

关于 MCP-Ollama Client

Lightweight MCP client that uses a local Ollama LLM to query multiple MCP servers defined in config.json

#python#ai-agents#chat-interface#llms#local-llm#function-calling#ollama#mcp-client

MCP-Ollama Client 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "MCP-Ollama-Client": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

MCP-Ollama Client 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP-Ollama Client?

MCP-Ollama Client is a command‑line chat client that runs entirely offline with a local LLM via Ollama and connects to any number of Model Context Protocol (MCP) servers declared in a single config.json. It is designed for developers who want to use local function‑calling models together with MCP tools.

How to use MCP-Ollama Client?

Clone the repository, set up a Python ≥ 3.12 virtual environment with uv, install dependencies, pull a local model with ollama pull qwen3:14b, edit the model name and server settings in config.json, then run uv run client.py. At startup the client launches every MCP server, aggregates their tool schemas with server‑name prefixes (e.g. postgres.*, filesystem.*), and presents the merged list to the LLM.

Key features of MCP-Ollama Client

  • Runs entirely offline with a local LLM via Ollama
  • Connects to multiple MCP servers side‑by‑side
  • Collision‑free tool names via server‑name prefixing
  • Everything configured in one config.json file
  • No cloud API keys required

Use cases of MCP-Ollama Client

  • Query a local database with natural language using MCP tools
  • Read or write files on a remote share through a filesystem MCP server
  • Combine data from multiple MCP sources in a single conversation
  • Test and debug MCP server integrations without a cloud LLM

常见问题

1

What is the default model and does it require cloud access?

The default model is `qwen3:14b`. Any function‑calling model that Ollama exposes will work. No cloud keys are required; everything runs locally.

2

What are the runtime dependencies?

Python ≥ 3.12, Ollama ≥ 0.8.0, and any MCP servers that support **stdio** transport.

3

How does tool naming work when multiple servers are used?

Tools are exposed as `<server>.<tool>` (for example `postgres.list_schemas`, `filesystem.read_file`) so names never clash, even when different servers provide tools with the same name.

4

Where is data stored and can I add or remove servers without changing code?

Data stays entirely local. Servers are defined in `config.json` under the `mcpServers` key; you can add or remove servers by editing that file without touching `client.py`.

5

What transport does the client use to communicate with MCP servers?

Each MCP server is started as its own **stdio** subprocess. No network transport is used between the client and the servers.

评论

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