node --version npm --version python --version

git clone https://github.com/Cam10001110101/mcp-server-ollama-deep-researcher cd mcp-server-ollama-deep-researcher npm install

# Windows pip install uv # macOS/Linux pip3 install uv

uv pip install .

pip install . # Windows pip3 install . # macOS/Linux

npm run build

ollama pull deepseek-r1:8b

git clone https://github.com/Cam10001110101/mcp-server-ollama-deep-researcher cd mcp-server-ollama-deep-researcher

cp .env.example .env # Edit the .env file with your API keys

chmod +x run-docker.sh

./run-docker.sh start

ollama pull deepseek-r1:8b # or your preferred model ollama serve

{ "mcpServers": { "ollama-deep-researcher": { "command": "node", "args": ["path/to/mcp-server-ollama-deep-researcher/build/index.js"], "env": { "LANGSMITH_TRACING": "true", "LANGSMITH_ENDPOINT": "https://api.smith.langchain.com", "LANGSMITH_API_KEY": "your-langsmith-key", "LANGSMITH_PROJECT": "ollama-deep-researcher-mcp-server", "TAVILY_API_KEY": "your-tavily-key", // Include tvly- prefix "PERPLEXITY_API_KEY": "your-perplexity-key", "PYTHONPATH": "path/to/mcp-server-ollama-deep-researcher/src" } } } }

"PYTHONUNBUFFERED": "1"

{ "mcpServers": { "ollama-deep-researcher": { "command": "docker", "args": ["exec", "-i", "ollama-deep-researcher-mcp", "node", "build/index.js"], "env": {} } } }

{ "name": "configure", "arguments": { "maxLoops": 3, "llmModel": "deepseek-r1:1.5b", "searchApi": "tavily" } }

{ "name": "research", "arguments": { "topic": "Austin LangChain, aimug.org" } }

{ "name": "get_status", "arguments": { "_dummy": "dummy" } }

npx @modelcontextprotocol/inspector node path/to/server/index.js --model llama3.2 --max-loops 3 --search-api tavily

Configure with perplexity and deepseek-r1:8b then research AI-First Applications

{ "searchApi": "perplexity", "llmModel": "deepseek-r1:8b", "maxLoops": 3 }

Research configuration updated: Max Loops: 3 LLM Model: deepseek-r1:8b Search API: perplexity ### Complete Research Results **Updated Summary: AI-First Applications in Healthcare: Focus on PathAI** 1. **Design Principles**: - PathAI exemplifies a data-driven approach by leveraging extensive datasets to train advanced AI models like PLUTO. - Collaboration is central to their success, integrating technology into clinical workflows through partnerships with institutions such as Cleveland Clinic. 2. **Industry Transformations**: - In healthcare, PathAI has significantly impacted diagnostic accuracy and efficiency. Their foundation model, PLUTO, demonstrates superior performance in various pathology tasks, outperforming existing models while reducing costs. - Collaborations with leading institutions have facilitated the creation of digital pathology data networks, enhancing research and clinical care. 3. **Scalability and Performance**: - PathAI's PLUTO model offers enhanced efficiency and compactness, significantly reducing training and inference costs. - This innovation underscores their commitment to scalable and effective solutions in healthcare. 4. **Growth and Impact**: - PathAI's growth strategy includes strategic partnerships and collaborations, such as their partnership with Cleveland Clinic and acquisition by Quest Diagnostics. - These moves accelerate AI and digital pathology adoption, particularly in cancer diagnosis. This summary highlights PathAI's contributions to healthcare through innovative technology and strategic collaborations, emphasizing their role in driving advancements and improving patient outcomes. ## Sources ### Perplexity Search 1 1. https://intelifaz.com/insights/ai-first-software-design 2. https://www.uxdesigninstitute.com/blog/how-to-design-for-ai-first-products/ 3. https://vux.world/ai-design-principles/ 4. https://www.leanware.co/insights/ai-first-apps 5. https://adamfard.com/blog/ai-ux-design-framework 6. https://www.sgh.com/insight/artificial-intelligence-best-practices/ 7. https://www.index.dev/blog/generative-ai-application-design-principles 8. https://onstrategyhq.com/resources/ai-guiding-principles/ 9. https://orangematter.solarwinds.com/2024/04/29/introducing-ai-by-design-principles-for-responsible-ai/ 10. https://principles.design/examples/10-principles-for-design-in-the-age-of-ai ### Perplexity Search 2 1. https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders 2. https://www.cloudera.com/resources/the-art-of-the-possible/ai-first-benefits-5-real-world-outcomes.html 3. https://builtin.com/artificial-intelligence/examples-ai-in-industry 4. https://www.uxforai.com/p/the-rise-of-ai-first-products 5. https://www.1051theblaze.com/ai-first-mobile-apps/ 6. https://www.techtarget.com/searchenterpriseai/tip/The-history-of-artificial-intelligence-Complete-AI-timeline 7. https://gitnation.com/contents/demystifying-ai-first-building-applications-for-the-future 8. https://fptsoftware.com/resource-center/blogs/the-ai-first-future-challenges-and-opportunities 9. https://online.maryville.edu/blog/history-of-ai/ 10. https://www.audience.io/blog/artificial-intelligence-first-party-data-the-future-of-data ### Perplexity Search 3 1. https://monday.com/blog/rnd/technical-specification/ 2. https://softwaremind.com/blog/8-steps-for-successful-software-implementation/ 3. https://www.infotech.com/research/ss/build-your-enterprise-application-implementation-playbook 4. https://interactiveimmersive.io/blog/touchdesigner-lessons/04-technical-implementation-design/ 5. https://www.indeed.com/career-advice/finding-a-job/technical-requirements 6. https://www.techtarget.com/searchcustomerexperience/definition/implementation 7. https://theobogroup.com/what-to-know-about-technical-implementations/ 8. https://www.ntietz.com/blog/whats-behind-just-implementation/ 9. https://public.cyber.mil/stigs/ ### Perplexity Search 4 1. https://www.ittbiomed.com/transforming-pathology-with-ai/ 2. https://www.pathai.com/resources/pathai-commences-new-year-with-strong-foundation-for-growth-following-key-2023-achievements-in-ai-powered-pathology-products-strategic-partnerships-and-leadership-team-hires/ 3. https://canvasbusinessmodel.com/blogs/target-market/pathai-target-market 4. https://www.pathologynews.com/industry-news/pathai-launches-new-pathologist-centric-features-on-aisight-to-enable-efficient-case-review-through-intelligent-case-prioritization-and-real-time-multi-institutional-collaboration/ 5. https://newsroom.clevelandclinic.org/2022/03/10/pathai-and-cleveland-clinic-announce-collaboration-to-build-digital-pathology-infrastructure-and-evolve-use-of-ai-powered-pathology-algorithms-in-research-and-clinical-care 6. https://academic.oup.com/jamia/article/30/9/1503/7174318 7. https://hitconsultant.net/2024/05/14/pathai-unveils-ai-foundation-model-built-for-disease-detection/ 8. https://newsroom.questdiagnostics.com/2024-05-01-Quest-Diagnostics-to-Acquire-PathAI-Diagnostics-to-Accelerate-AI-and-Digital-Pathology-Adoption-in-Cancer-Diagnosis-Forms-Licensing-Agreements-with-PathAI 9. https://www.multimodal.dev/post/ai-in-healthcare 10. https://canvasbusinessmodel.com/blogs/growth-strategy/pathai-growth-strategy

Key Principles of AI-First Applications: 1. Human-AI Collaboration - Applications are designed with AI as a core component rather than an add-on - Focus on augmenting human capabilities rather than replacing them - Clear feedback loops between AI systems and human users 2. Data-Centric Architecture - Built around robust data pipelines and continuous learning - Strong emphasis on data quality and governance - Scalable infrastructure to handle growing datasets 3. Adaptive User Experience - Interfaces that evolve based on user interaction patterns - Contextual awareness and personalization - Clear communication of AI capabilities and limitations Real-World Example: PathAI in Healthcare Core Technology: - PLUTO foundation model demonstrates superior performance in pathology tasks - Focuses on efficiency and cost reduction while maintaining high accuracy - Built for scalability and integration into existing workflows Implementation Strategy: - Strategic partnerships with healthcare institutions like Cleveland Clinic - Creation of digital pathology data networks - Focus on practical clinical applications and research Impact: - Improved diagnostic accuracy in cancer detection - Enhanced efficiency in pathology workflows - Reduced costs through optimized resource utilization Best Practices for AI-First Applications: 1. Technical Implementation - Start with clear use cases and success metrics - Build robust data pipelines and validation processes - Implement continuous monitoring and model updating 2. User Experience - Design transparent interfaces that build trust - Provide clear feedback on AI decisions and confidence levels - Allow for human oversight and intervention 3. Integration and Scaling - Focus on seamless integration with existing workflows - Build scalable infrastructure from the start - Plan for continuous improvement and iteration