Health Gorilla MCP Server for Pydantic AI 12 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Health Gorilla through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Health Gorilla "
"(12 tools)."
),
)
result = await agent.run(
"What tools are available in Health Gorilla?"
)
print(result.data)
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 Pydantic AI via MCP
Follow these steps to integrate the Health Gorilla MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 12 tools from Health Gorilla with type-safe schemas
Why Use Pydantic AI with the Health Gorilla MCP Server
Pydantic AI provides unique advantages when paired with Health Gorilla through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Health Gorilla integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Health Gorilla connection logic from agent behavior for testable, maintainable code
Health Gorilla + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Health Gorilla MCP Server delivers measurable value.
Type-safe data pipelines: query Health Gorilla with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Health Gorilla tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Health Gorilla and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Health Gorilla responses and write comprehensive agent tests
Health Gorilla MCP Tools for Pydantic AI (12)
These 12 tools become available when you connect Health Gorilla to Pydantic AI 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 Pydantic AI
Common issues when connecting Health Gorilla to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiHealth Gorilla + Pydantic AI FAQ
Common questions about integrating Health Gorilla MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.