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基本信息
暂无概览
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"ollama-MCP-server": {
"command": "python",
"args": [
"-m",
"unittest",
"discover"
]
}
}
}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。
ollama-MCP-server is a Model Context Protocol (MCP) server that communicates with a local Ollama LLM instance, providing task decomposition, result evaluation, model management, and workflow management for MCP-compatible applications.
Install the package with pip install ollama-mcp-server, then configure the server in your MCP client (e.g., Claude Desktop) using the claude_desktop_config.json file. Tools such as add-task, decompose-task, evaluate-result, and run-model are invoked via the MCP protocol.
The server requires Python, the `ollama-mcp-server` package, and a local installation of Ollama with at least one model pulled (e.g., `llama3`). Environment variables like `OLLAMA_HOST`, `DEFAULT_MODEL`, and `LOG_LEVEL` can be set.
Model selection follows a priority order: (1) the `model` parameter in a tool call, (2) the `env` section of the MCP configuration file, (3) the `OLLAMA_DEFAULT_MODEL` environment variable, and (4) the default value (`llama3`).
The server uses connection pooling (max 10 concurrent connections per host) and an LRU cache (max 100 entries) to reduce response times and lower Ollama server load. These settings are adjustable in `config.py`.
Errors return a structured JSON response with a `message`, `status_code`, and optional `details` (e.g., the provided ID for a missing task). If a model is not found, the error message includes the list of available models.
The server communicates over stdio using the MCP protocol. The README does not mention authentication mechanisms; access depends on local Ollama and MCP client configuration.
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