MCP Server: Ollama Deep Researcher

Name: MCP Server: Ollama Deep Researcher
Author: Cam10001110101

@Cam10001110101

代码仓库

关于 MCP Server: Ollama Deep Researcher

暂无概览

基本信息

分类

AI 与智能体

许可证

MIT

运行时

python

传输方式

stdio

发布者

Cam10001110101

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-server-ollama-deep-researcher": {
      "command": "node",
      "args": [
        "dist/index.js"
      ]
    }
  }
}

工具

未检测到工具

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 Ollama Deep Researcher?

Ollama Deep Researcher is a desktop extension that enables advanced topic research using web search APIs and LLM synthesis, powered by a local MCP server over stdio. It is designed for integration with the DXT ecosystem and supports configurable research parameters, status tracking, and resource access.

How to use Ollama Deep Researcher?

Install Node.js and Python dependencies, set required environment variables for a web search API (Tavily, Perplexity, or Exa), optionally configure an LLM endpoint, then build and run the server with node dist/index.js. Use the research tool with a topic, get_status to check progress, and configure to adjust parameters like maxLoops, llmModel, or searchApi.

Key features of Ollama Deep Researcher

MCP protocol over stdio for local, secure operation
Defensive programming: error handling, timeouts, and validation
Logging and debugging output via stderr
Compatible with DXT host environments
Research subprocesses killed after 30 minutes to prevent hangs

Use cases of Ollama Deep Researcher

Research any topic using web search and LLM synthesis
Track the status of ongoing research tasks
Access research results as MCP resources
Configure LLM model and search API without code changes

FAQ from Ollama Deep Researcher

What web search APIs are supported?

Tavily, Perplexity, and Exa are supported. Each requires its own API key set as an environment variable.

What LLMs can I use?

Ollama, DeepSeek, and other LLMs compatible with the configuration are supported. The LLM model can be set via the configure tool.

How do I prevent plaintext API keys from being stored on disk?

Optionally use 1Password Environments to mount a local .env file as a named pipe, or use op run to inject secrets at launch. Details are provided in the README under “Optional: secure secrets with 1Password”.

What happens if a research task takes too long?

Research subprocesses are automatically killed after 30 minutes to prevent hangs. Timeouts are logged to stderr.

What are the runtime dependencies?

Node.js for the TypeScript MCP server, Python for the research assistant, and API keys for the chosen web search service. All communication occurs locally over stdio.