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metadata
title: 'MediAgent AI: Conversational Health Navigator & MCP Tool Server'
emoji: 🧬
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: false
secrets:
  - MODAL_BACKEND_URL
tags:
  - mcp-server-track
  - agent-demo-track

🧬 MediAgent AI: Conversational Health Navigator & MCP Tool Server

➡️ Try the Live Demo Here!

Powered by Modal and LlamaIndex. Special thanks for their support!

MediAgent AI is a sophisticated, conversational health assistant and a fully MCP-compliant tool server, built on Modal and Gradio. It provides reliable, sourced, and role-adapted answers to a wide range of medical questions.

This project is a submission for the Gradio Agents & MCP Hackathon 2025, competing in two tracks:

  • 🔧 MCP Tool / Server Track: Our backend exposes a robust, MCP-compliant endpoint for any agent to use.
  • 🤖 Agentic Demo Showcase Track: The Gradio UI provides a powerful demonstration of an agent using these tools to help users.

🌟 Key Features

  • 🌐 Multilingual Support: Automatically detects and responds in English, Spanish, Hindi, and French.
  • 👤 Role-Based Adaptation:
    • Patient Mode: Simplified, easy-to-understand health information.
    • Doctor Mode: In-depth, technical insights using specialized medical databases.
  • 🛡️ Critical Safety Layer: Identifies keywords related to medical emergencies (e.g., "chest pain") and immediately instructs the user to seek professional help, bypassing the AI.
  • 🔗 Sourced & Verifiable: All information is backed by citations from authoritative sources.
  • 💬 Stateful Conversations: Remembers chat history for contextual follow-up questions.
  • 🛠️ Fully Compliant MCP Tool Server: Our tools can be called externally by any MCP client.

🛠️ Tools & Technology Stack

Our agent leverages a powerful, specialized toolkit to provide accurate information. These tools are exposed via our public MCP endpoint.

Tool Name Description Data Source(s)
search_web For general health queries and patient-level explanations. DuckDuckGo
search_pubmed Accesses peer-reviewed medical literature for evidence-based information. PubMed / NCBI
get_drug_label_info Retrieves detailed, official FDA-approved prescribing information. openFDA / DailyMed
check_drug_side_effects Finds known side effects for a specific medication. openFDA / DailyMed
check_drug_interactions Checks for adverse interactions between two or more drugs. RxNorm API (NIH)
search_clinical_trials Discovers ongoing or completed clinical studies for conditions/treatments. ClinicalTrials.gov (NIH)

Core Technologies:

  • Backend & Compute: Modal for scalable, serverless GPU and CPU functions.
  • AI Agent Framework: LlamaIndex for orchestrating the agent's think-plan-act loop.
  • LLM: Mistral 7B for high-quality reasoning.
  • Frontend UI: Gradio for the interactive chat interface.

🏛️ Architecture: Hybrid Single-Server MCP

Our single Modal application serves as both the core Agent and its own MCP-compliant Tool Server.

  1. Agent Endpoint (/): A private endpoint that takes a user query and orchestrates the full agentic workflow (planning, tool selection, synthesis).
  2. MCP Tool Endpoint (hidden in backend): A public endpoint that exposes our individual tools. This makes our application a valid MCP Server, proving compliance and allowing any external MCP client to use our medical tools.

🚀 Test our MCP Tool Server Live!

You can call our tools from any MCP-compatible client or directly with curl.

⚠️ Note: The first call may take 30-60 seconds as the serverless backend spins up. Subsequent calls are much faster.

# Example: Check for a critical drug interaction
curl -X POST 'https://sunilpanda992--medi-agent-ai-final-backend-mediagentai-m-3f699b.modal.run' \
--header 'Content-Type: application/json' \
--data '{
    "tool_name": "check_drug_interactions",
    "tool_args": {
      "drugs": ["warfarin", "amiodarone"]
    }
  }'

# Example: Search for PubMed articles
curl -X POST 'https://sunilpanda992--medi-agent-ai-final-backend-mediagentai-m-3f699b.modal.run' \
--header 'Content-Type: application/json' \
--data '{
    "tool_name": "search_pubmed",
    "tool_args": {"query": "metformin AND cardiovascular outcomes", "max_results": 2}
}'

# Example: Get official FDA label information (CORRECTED)
curl -X POST 'https://sunilpanda992--medi-agent-ai-final-backend-mediagentai-m-3f699b.modal.run' \
--header 'Content-Type: application/json' \
--data '{
    "tool_name": "get_drug_label_info",
    "tool_args": {"drug_name": "atorvastatin"}
}'

IGNORE_WHEN_COPYING_START Use code with caution. Markdown IGNORE_WHEN_COPYING_END

🧪 Try It Out in the App!

Select the appropriate "patient" or "doctor" role in the chat interface above and try these prompts:

Patient Query: "What are the most common side effects of metformin?"

Doctor Query: "My patient with Crohn's is on methotrexate and we are considering adding infliximab. I need the latest PubMed literature on efficacy and a check for interactions between the two drugs."

Spanish Query: "¿Cuáles son los síntomas de la diabetes tipo 2?"

Interaction Check: "Can I take ibuprofen with lisinopril?"