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
└── ...

Installation & Setup

Clone the repository and install dependencies:

git clone 
cd mcp-server-ollama-deep-researcher
npm install

Install Python dependencies for the assistant:

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

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:
```
export TAVILY_API_KEY=your_tavily_key
export PERPLEXITY_API_KEY=your_perplexity_key
export EXA_API_KEY=your_exa_key
```
Build the TypeScript server (if needed):
```
npm run build
```

Run the extension locally for testing:

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

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.

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.

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

mcp-server-ollama-deep-researcher

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README