Health Gorilla MCP. Manage the entire lab order lifecycle from chat.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Health Gorilla connects the entire lab workflow to your AI client via MCP. Manage patient records, submit new lab orders, and retrieve structured results—all through conversation.
You can create patient profiles, check order statuses, and pull longitudinal result sets for EHR integration. It handles the full cycle: from initial patient match to final result review.
What your AI agents can do
Cancel lab order
Stops a pending lab order. Use this when an order was submitted in error or clinical circumstances changed.
Create patient record
Registers a new patient in the system, returning a unique patient ID for subsequent orders.
Get lab results
Retrieves detailed, structured lab results for a specific, completed order, including sign-off details.
Registers new patient records or pulls demographic details for existing patients, ensuring accurate identity matching before any ordering takes place.
Searches for correct test codes, submits the order to the network, and monitors the order status through the entire collection and processing lifecycle.
Pulls detailed, structured data for a specific completed order, including critical values and pathologist sign-off for clinical review.
Aggregates all historical lab results for a single patient, allowing users to analyze long-term data patterns across multiple visits and orders.
Finds detailed information on providers or lists all orders for a patient/department, helping narrow the scope of work.
Ask AI about this MCP
Supported MCP Clients
Gemini Health Gorilla MCP Server: 12 Tools for Lab Workflow
These twelve tools let your AI agent handle every step of the lab process: from checking demographics to submitting complex test orders and pulling structured results.
019d75aecancel lab order
Stops a pending lab order. Use this when an order was submitted in error or clinical circumstances changed.
019d75aecreate patient record
Registers a new patient in the system, returning a unique patient ID for subsequent orders.
019d75aeget lab results
Retrieves detailed, structured lab results for a specific, completed order, including sign-off details.
019d75aeget order status
Checks the current progress and status of a submitted lab order (e.g., 'testing', 'completed').
019d75aeget patient demographics
Pulls demographic and contact information for a specific, registered patient.
019d75aeget provider details
Gets detailed information and credentials for a specific healthcare provider.
019d75aelist orders
Lists recent lab orders, allowing filters by patient ID or status (e.g., 'pending', 'completed').
019d75aelist patient results
Retrieves all historical lab results for a patient, useful for tracking long-term trends.
019d75aematch patient
Compares a patient against existing records to avoid creating duplicate profiles.
019d75aesearch lab tests
Searches the lab catalog by test name, LOINC code, or category to find required test codes.
019d75aesearch providers
Searches the network for healthcare providers by specialty or location.
019d75aesubmit lab order
Submits a new lab or radiology order to the diagnostic network, which returns a tracking ID.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Health Gorilla, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Health Gorilla MCP Server - Lab Orders & Results
This server connects your AI client directly to the Health Gorilla diagnostic network. You'll manage the full lab workflow—from setting up patient files to getting final results—all through conversation. You don't have to jump between different systems to track care.
Creating and Verifying Patient Profiles
Before you order anything, you gotta make sure the patient record is solid. You can use match_patient to compare a patient against existing records, keeping you from creating duplicate profiles. If the match isn't there, you use create_patient_record to register a brand new patient and get a unique ID. You can then pull basic info using get_patient_demographics to confirm the right name and contact details.
Submitting and Tracking Lab Orders
When it's time to order tests, you first need the right codes. You can run search_lab_tests to look up the lab catalog by test name, LOINC code, or category. Once you have the codes, you submit the order using submit_lab_order, which sends the request to the diagnostic network and gives you a tracking ID.
You can check the order's progress anytime with get_order_status to see if it's 'testing' or 'completed.' You can also list all recent orders for a patient or department using list_orders. If something changes—maybe the patient cancels—you use cancel_lab_order to stop a pending order.
Getting and Reviewing Results
When the lab is done, you get the results. You use get_lab_results to pull detailed, structured data for a specific, completed order, including critical values and the pathologist's sign-off. For a bigger picture, you use list_patient_results to grab every historical lab result for a patient, letting you spot long-term trends over multiple visits.
You can also find details on providers using get_provider_details or find a specialist using search_providers or search_providers.
Workflow Management
- You can use
list_ordersto audit all recent lab work for a patient or department. - You can use
list_patient_resultsto aggregate all results for a patient, letting you track data patterns over time. - You can use
get_patient_demographicsto pull a patient's contact info before submitting an order. - You can use
get_provider_detailsto verify a doctor's credentials. - You can use
search_lab_teststo confirm the correct test codes before ordering. - You can use
match_patientto prevent data duplication when starting a new case.
This server handles everything from the initial patient ID check to the final result review.
How Health Gorilla MCP Works
- 1 First, call
match_patientto confirm the patient's identity against the Health Gorilla network. This prevents data duplication and ensures you're working with the correct record. - 2 Next, use
search_lab_teststo find the correct LOINC/CPT codes for the tests needed. Then, callsubmit_lab_orderusing those codes and the patient ID. - 3 Finally, use
get_order_statusto track the order's progress. Once the status is 'completed', callget_lab_resultsto get the structured data.
The bottom line is, your agent handles the entire process—from identifying the person to pulling the final, structured result—without you having to remember the correct sequence of calls.
Who Is Health Gorilla MCP For?
Clinicians, hospital administrators, and medical coding staff who deal with high volumes of patient data. If your job involves verifying a patient's identity, placing tests, or reviewing longitudinal charts, this is for you. You're tired of manual data entry, chasing down faxed results, and making sure the right patient gets the right test.
Uses the agent to confirm patient demographics via get_patient_demographics and then submits the appropriate test via submit_lab_order based on clinical needs.
Uses the agent to check the status of pending orders with get_order_status and retrieve results with get_lab_results to update the EHR.
Uses the agent to verify provider credentials (get_provider_details) and list completed orders (list_orders) to ensure billing accuracy.
What Changes When You Connect
- Full Patient Context: Use
match_patientbefore anything else. You get immediate confirmation that the patient record you're using isn't a duplicate, which is critical for accurate billing and results attribution. - Structured Results: Don't read PDFs.
get_lab_resultsdelivers structured data—timestamp, pathologist sign-off, and critical values—ready to dump right into your EHR system. - Historical View: Instead of checking 10 different folders,
list_patient_resultspulls every test result for a patient. This makes spotting trends (like rising HbA1c) easy. - Streamlined Ordering: Finding a test used to mean searching 5 different catalogs. Now,
search_lab_testsfinds the LOINC/CPT code and test details in one call, letting you runsubmit_lab_orderfaster. - Status Visibility: Never call a doctor to ask, 'When will the results be ready?' Just call
get_order_status. It tells you exactly where the specimen is and when processing finishes. - Error Correction: Submitted a test by accident?
cancel_lab_orderhandles it. You can stop a pending order using the tool, keeping records clean.
Real-World Use Cases
The new patient walk-in
A clinic staff member needs to process a patient who's never been seen. They first use match_patient to confirm no record exists, then create_patient_record to make the profile. They use search_lab_tests to get the correct codes, and finally, submit_lab_order to get the tracking ID.
Reviewing chronic disease patterns
A specialist needs to check a patient's long-term health. They run list_patient_results to pull every available result for the past year. They then review the values to track trends—for example, tracking lipid panel changes over time—without having to download and manually compare dozens of reports.
Following up on a critical test
A nurse calls the agent: 'Check the status of the recent CBC.' The agent calls get_order_status. If the status is 'testing', the nurse knows to follow up later. If it's 'completed', the nurse can then call get_lab_results to get the actual values.
Verifying a referral provider
A physician needs to send a referral to a specialist. They use search_providers to find the correct specialist's NPI number and confirm network participation using get_provider_details before submitting the referral.
The Tradeoffs
Assuming data is clean
Running submit_lab_order with a patient ID that might be outdated or duplicated. This risks submitting tests against the wrong person, which is a major data integrity violation.
→
Always run match_patient first. If the match score is low, manually verify the identity. Then, call get_patient_demographics to pull the most current contact and MRN data before proceeding.
Manual status checks
Calling the lab and waiting on hold to ask if the results are ready. This wastes time and relies on human availability, creating a workflow bottleneck.
→
Use get_order_status to check the real-time progress. This gives you the specimen collection status, lab processing info, and an estimated completion time, all without calling anyone.
Retrieving results piecemeal
Calling get_lab_results once, then calling list_patient_results later, making it hard to correlate the data. You end up with separate data chunks that don't tell the full story.
→
Use list_patient_results to gather all historical data first. Then, use get_lab_results only when you need the final, detailed data for a specific, recent order ID.
When It Fits, When It Doesn't
Use this server if your process requires tracking a patient's data through multiple, distinct stages: identity verification, order submission, status monitoring, and result retrieval. You need a single source of truth for the entire lab cycle.
Don't use this if your only need is to look up a simple name or phone number; get_patient_demographics handles that fine. But if you need to know if that person exists in the system or if the data is current, you need the full stack. You're building a process, not just a lookup.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Health Gorilla. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 12 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Tracking a patient's lab work shouldn't require logging into three different portals.
Before, you'd have to open the EHR, search for the patient's record, copy the MRN, then log into the lab portal to see the order status. If you needed history, you'd jump to a separate reporting tool and cross-reference the dates. It's a mess of tabs, copy-pasting, and manual data stitching.
With Health Gorilla MCP, you talk to your agent. You ask, 'What's the status of John Smith's recent blood work?' The agent handles the patient ID lookup, checks the order status with `get_order_status`, and returns a single, synthesized answer—no context switching required.
Health Gorilla MCP Server: Managing Lab Orders & Patient Data
The process of submitting a lab order used to be a paper trail: doctor writes it out, nurse enters it, and the front desk files it. Now, your agent uses `search_lab_tests` to find the correct LOINC code, and then calls `submit_lab_order` directly through the system. The order goes right into the network, automatically.
The system handles the handoffs. You just need to tell your agent the goal. It takes care of the patient ID, the codes, and the submission, giving you an order ID you can track instantly.
Common Questions About Health Gorilla MCP
How do I use `match_patient` with Health Gorilla MCP Server? +
You run match_patient first. It returns a match score and potential records. Use this to confirm the patient's identity and ensure you don't accidentally create a duplicate record before running any other tools.
Can I retrieve lab results using `get_lab_results`? +
Yes. get_lab_results pulls the final, structured data for a specific order ID. This data includes the completion timestamp and pathologist sign-off, making it suitable for immediate EHR integration.
What if I need to check multiple orders for one patient? Use `list_orders` or `list_patient_results`? +
Use list_orders to see a list of all submitted requests, while list_patient_results pulls the actual test values and dates for trend analysis. Choose based on whether you need the order metadata or the result data.
How do I submit a new lab order using `submit_lab_order`? +
You must first get the patient ID (via match_patient or get_patient_demographics), find the test codes (via search_lab_tests), and then pass all that info to submit_lab_order. The tool requires specific codes (LOINC/CPT) and the patient ID.
How do I verify patient identity before creating a new record using `get_patient_demographics`? +
Use get_patient_demographics to confirm basic identity details. This tool returns the name, DOB, gender, and contact info, letting you verify the patient exists and match the information against your internal system before proceeding.
What tools should I use to find available tests or specialty providers? Should I use `search_lab_tests` or `search_providers`? +
You need different tools for different searches. Use search_lab_tests to find test codes (LOINC/CPT) and categories. Use search_providers to locate specific healthcare providers or specialties.
If an order is wrong, how do I cancel it using `cancel_lab_order`? +
You use cancel_lab_order for orders that are 'received' or 'pending' status. Remember, the order must be in one of those states; if it's already 'collected' or 'testing', you'll need to notify the lab manually.
How can I check the current progress of an existing order using `get_order_status`? +
Run get_order_status to track an order's progress. This function returns the current status—like 'collected', 'testing', or 'completed'—along with specimen collection and lab processing details.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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