Overview

The Cameron Rohn enables AI models to perform advanced topic research using web search and LLM synthesis. It's part of the Model Context Protocol (MCP) system, providing a safe and standard way to connect AI with research capabilities. For more advanced memory capabilities, consider integrating with a Knowledge Graph Memory or Supavec. You can also explore other AI/ML tools for enhanced functionality.

Created by:

Developed by

Key Features

🔍

Web Search Integration

Utilize web search APIs (Tavily, Perplexity) to gather up-to-date information.

🧠

LLM Synthesis

Synthesize research findings using local Ollama LLMs for comprehensive answers.

🔄

Iterative Research Process

Iteratively improve summaries by identifying knowledge gaps and generating new search queries.

📝

Markdown Summary with Sources

Provides a final markdown summary of research with all sources used.

Available Tools

Quick Reference

Tool	Purpose	Category
`research`	Research a given topic	Research
`get_status`	Get the status of ongoing research	Utility
`configure`	Configure research parameters	Configuration

Detailed Usage

research▶

Initiate a deep research process on a specified topic.

use_mcp_tool({
  server_name: "ollama-deep-researcher",
  tool_name: "research",
  arguments: {
    topic: "The impact of AI on climate change"
  }
});

Returns a research ID to track progress.

get_status▶

Retrieve the current status and results of an ongoing research task.

use_mcp_tool({
  server_name: "ollama-deep-researcher",
  tool_name: "get_status",
  arguments: {
    researchId: "research-12345"
  }
});

Returns the current status, progress, and final summary if completed.

configure▶

Configure parameters for the research process, such as max loops, LLM model, and search API.

use_mcp_tool({
  server_name: "ollama-deep-researcher",
  tool_name: "configure",
  arguments: {
    maxLoops: 5,
    llmModel: "deepseek-r1:8b",
    searchApi: "tavily"
  }
});

Updates the research configuration for subsequent tasks.

Installation

{
  "mcpServers": {
    "ollama-deep-researcher": {
      "command": "node",
      "args": [
        "path/to/mcp-server-ollama-deep-researcher/build/index.js"
      ],
      "env": {
        "TAVILY_API_KEY": "your_tavily_key",
        "PERPLEXITY_API_KEY": "your_perplexity_key",
        "LANGSMITH_API_KEY": "your-langsmith-key"
      }
    }
  }
}

Environment Variables:

Ensure TAVILY_API_KEY, PERPLEXITY_API_KEY, and LANGSMITH_API_KEY are set in your environment or directly in the env object.

Common Use Cases

1. Automated Literature Review

Perform systematic literature reviews on specific topics, aggregating information from various web sources.

2. Knowledge Gap Analysis

Identify missing information or inconsistencies in existing knowledge bases by iteratively researching and synthesizing findings.

3. Research Synthesis for Reports

Generate comprehensive summaries and reports on complex subjects, complete with citations and source validation.

Sources

ConsoleSpy MCP Server

ConsoleSpy MCP servers enable AI models to interact with browser console logs, providing capabilities for real-time debugging, error monitoring, and application analysis.

ArangoDB MCP Server

ArangoDB MCP servers enable AI models to interact with ArangoDB databases, providing capabilities for document operations, graph queries, AQL queries, and multi-model data management.

Supavec MCP Server: Vector Database for AI Applications

Supavec MCP servers enable AI models to interact with vector databases, providing capabilities for storing, searching, and managing vector embeddings for AI applications.

Ollama Deep Researcher: AI Model for Web Search & LLM Synthesis