Ollama Deep Researcher DXT Extension

Overview

Ollama Deep Researcher is a Desktop Extension (DXT) that enables advanced topic research using web search and LLM synthesis, powered by a local MCP server. It supports configurable research parameters, status tracking, and resource access, and is designed for seamless integration with the DXT ecosystem.

• Research any topic using web search APIs (Tavily, Perplexity, Exa) and LLMs (Ollama, DeepSeek, etc.)
• Configure max research loops, LLM model, and search API
• Track status of ongoing research
• Access research results as resources via MCP protocol

Features

• Implements the MCP protocol over stdio for local, secure operation
• Defensive programming: error handling, timeouts, and validation
• Logging and debugging via stderr
• Compatible with DXT host environments

Directory Structure

.
├── manifest.json         # DXT manifest (see MANIFEST.md for spec)
├── src/
│   ├── index.ts         # MCP server entrypoint (Node.js, stdio transport)
│   └── assistant/       # Python research logic
│       └── run_research.py
├── README.md            # This documentation
└── ...

Manifest

See manifest.json for the full DXT manifest, including tool schemas and resource templates. Follows DXT MANIFEST.md.

Logging & Debugging

• All server logs and errors are output to stderr for debugging.
• Research subprocesses are killed after 5 minutes to prevent hangs.
• Invalid requests and configuration errors return clear, structured error messages.

Security & Best Practices

• All tool schemas are validated before execution.
• API keys are required for web search APIs and are never logged.
• MCP protocol is used over stdio for local, secure communication.

Testing & Validation

• Validate the extension by loading it in a DXT-compatible host.
• Ensure all tool calls return valid, structured JSON responses.
• Check that the manifest loads and the extension registers as a DXT.

Search API Comparison

• Tavily: Fast, comprehensive web search with raw content extraction
• Perplexity: AI-powered search with natural language summaries and citations
• Exa: Neural search engine optimized for semantic search with highlights

References

• DXT Architecture Overview
• DXT Manifest Spec
• DXT Example Extensions
• Model Context Protocol SDK

Installation & Setup

1. Clone the repository and install dependencies:

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   git clone <your-repo-url>
   cd mcp-server-ollama-deep-researcher
   npm install

2. Install Python dependencies for the assistant:

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   cd src/assistant
   pip install -r requirements.txt
   # or use pyproject.toml/uv if preferred

3. Set required environment variables for web search APIs:

   • For Tavily: TAVILY_API_KEY
   • For Perplexity: PERPLEXITY_API_KEY
   • For Exa: EXA_API_KEY (Get yours at https://dashboard.exa.ai/api-keys)
   • Example:
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     export TAVILY_API_KEY=your_tavily_key
     export PERPLEXITY_API_KEY=your_perplexity_key
     export EXA_API_KEY=your_exa_key

4. Build the TypeScript server (if needed):

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   npm run build

5. Run the extension locally for testing:

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   node dist/index.js
   # Or use the DXT host to load the extension per DXT documentation

Usage

• Research a topic:
  • Use the research tool with { "topic": "Your subject" }
• Get research status:
  • Use the get_status tool
• Configure research parameters:
  • Use the configure tool with any of: maxLoops, llmModel, searchApi

Troubleshooting

• Missing API key: Ensure TAVILY_API_KEY, PERPLEXITY_API_KEY, or EXA_API_KEY is set in your environment depending on which search API you're using.
• Python errors: Check Python dependencies and logs in stderr.
• Timeouts: Research subprocesses are limited to 5 minutes.