Health Gorilla MCP Server for LangChain 12 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Health Gorilla through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"health-gorilla": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Health Gorilla, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Health Gorilla MCP Server
Connect Health Gorilla to any AI agent via MCP.
How to Connect Health Gorilla to LangChain via MCP
Follow these steps to integrate the Health Gorilla MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 12 tools from Health Gorilla via MCP
Why Use LangChain with the Health Gorilla MCP Server
LangChain provides unique advantages when paired with Health Gorilla through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Health Gorilla MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Health Gorilla queries for multi-turn workflows
Health Gorilla + LangChain Use Cases
Practical scenarios where LangChain combined with the Health Gorilla MCP Server delivers measurable value.
RAG with live data: combine Health Gorilla tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Health Gorilla, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Health Gorilla tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Health Gorilla tool call, measure latency, and optimize your agent's performance
Health Gorilla MCP Tools for LangChain (12)
These 12 tools become available when you connect Health Gorilla to LangChain via MCP:
cancel_lab_order
Orders in "received" or "pending" status can typically be cancelled. Orders already in "collected" or "testing" status cannot be cancelled and require lab notification. A cancellation reason is recommended for audit purposes. Use this when an order was submitted in error, the patient refused testing, or clinical circumstances have changed. Cancel a pending laboratory order
create_patient_record
Required fields: first name, last name, date of birth, and gender. Optional: address, phone, email, MRN (Medical Record Number), and insurance information. Use this to register a new patient before submitting lab orders. Returns the patient ID for use in subsequent order submissions. Create a new patient record in the Health Gorilla system
get_lab_results
Returns structured data suitable for EHR integration or clinical review. Results include timestamp of completion, pathologist sign-off (if applicable), and any critical value notifications. Use this to review patient results, identify abnormal values, or populate EHR records. Retrieve detailed laboratory results for a specific completed order
get_order_status
Status values include: "received", "in_progress", "collected", "testing", "completed", "cancelled". Returns order details, specimen collection status, lab processing information, and estimated completion time. Use this to track order progress, update patients on result timelines, or verify completion status. Check the current status of a submitted laboratory order
get_patient_demographics
Returns name, DOB, gender, contact information, MRN, and registration date. Use this to verify patient identity before order submission or to review patient registration details. Get demographic information for a registered patient
get_provider_details
Use this to verify provider credentials, obtain contact information for referrals, or confirm network participation before ordering tests. Get detailed information about a specific healthcare provider
list_orders
Optional filters: status (e.g., "pending", "completed", "cancelled") and patient_id. Each order includes order ID, patient name, test names, status, order date, and performing lab. Use this to review recent orders, track pending work, or audit ordering patterns. List laboratory orders with optional filtering by status or patient
list_patient_results
Includes test names, values, dates, and order references. Useful for trend analysis and longitudinal patient monitoring (e.g., tracking HbA1c over time, monitoring lipid panels). Use this for chronic disease management, preventive care follow-up, or comprehensive patient history review. List all laboratory results for a specific patient across all orders
match_patient
Returns match score and potential matches. Use this before creating new orders to avoid duplicate patient records and ensure results are attributed to the correct patient. Critical for data integrity in healthcare systems. Match a patient against existing records in the Health Gorilla network
search_lab_tests
Returns test names, LOINC codes, categories (chemistry, hematology, microbiology, etc.), turnaround times, and performing laboratory information. Use this to find the correct test codes (LOINC/CPT) before submitting orders, explore available diagnostic options, or verify test availability. Optional query parameter accepts free-text search. Optional category parameter filters by test type. Search the Health Gorilla lab test catalog by name, LOINC code, or category
search_providers
Results include provider name, specialty, NPI number, location, and contact information. Use this to find ordering providers, verify network participation, or locate specialists in a specific area. Optional filters: specialty (e.g., "Internal Medicine", "Cardiology") and location. Search for healthcare providers in the Health Gorilla network
submit_lab_order
The order includes patient demographics, ordering provider information, requested tests (LOINC/CPT codes), clinical indication/diagnosis (ICD-10 codes), and specimen collection details. Returns an order ID for tracking status and retrieving results. Use this to place lab orders electronically without manual paperwork. Supported test types include chemistry panels, CBC, metabolic panels, infectious disease testing, genetic testing, and radiology orders. The order is routed to the appropriate performing laboratory (Quest, LabCorp, etc.). Submit a new laboratory or radiology order through the Health Gorilla diagnostic network
Troubleshooting Health Gorilla MCP Server with LangChain
Common issues when connecting Health Gorilla to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersHealth Gorilla + LangChain FAQ
Common questions about integrating Health Gorilla MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Health Gorilla with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Health Gorilla to LangChain
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.