Redox MCP for AI. Access structured clinical data from natural language.
Works with every AI agent you already use
…and any MCP-compatible client








How this MCP server connects to your AI agent
Redox connects your AI agent directly to complex, standardized clinical data using FHIR and Redox Data Models. Search for patient records by identifier, pull active diagnoses, and write structured vital signs back to the EHR—all from a single chat interface.
What AI agents can do with Redox Automation
Create observation
This tool saves vitals or general observations directly back into the electronic health record.
Post data model
It sends a structured Redox Data Model event, useful for generalized system notifications.
Search condition
This tool retrieves active diagnoses and problem lists associated with a specific patient ID.
The agent searches connected systems for a patient using identifiers like MRNs or specific slugs.
It fetches a list of active diagnoses and conditions linked to a given patient ID, providing immediate medical context.
The agent sends structured data bundles (FHIR) to record new vital signs or clinical observations directly into the EHR.
It transmits asynchronous notifications or queries using standardized Redox JSON Data Models for broad system compatibility.
Ask an AI about this
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What AI agents can do with Redox: 4 Tools for Healthcare Interoperability
These tools allow you to programmatically manage core patient data tasks, including searching records, retrieving diagnoses, and writing structured observations via the FHIR standard.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Redox on VinkiusCreate Observation
This tool saves vitals or general observations directly back into the electronic health record.
Post Data Model
It sends a structured Redox Data Model event, useful for generalized system...
Search Condition
This tool retrieves active diagnoses and problem lists associated with a specific...
Search Patient
It searches for a patient record across the connected Redox FHIR API using...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
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 Redox, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ 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
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Redox. 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.
VINKIUS INFRASTRUCTURE
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Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
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EU data residency
Token Compression
~60% cost reduction
Built on the Model Context Protocol (MCP) for 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 connection provides 4 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The headache of stitching together a patient record from different systems., Solved with Vinkius AI Gateway
Right now, if you need to know everything about a patient—their current vitals, their active diagnoses, and who they are—you have to click across three or four different tabs and maybe even log into two separate portals. You pull the name from one system, copy the condition list from another, and then manually input the blood pressure reading somewhere else. It’s slow, it's error-prone, and half the time you end up with data that isn't structured enough to be useful.
With this MCP, your agent handles all those clicks. You ask a single question, like 'What is P123's full clinical picture?' The agent executes multiple calls—searching for identity, retrieving conditions, and pulling demographic data—and hands you one clean, actionable answer. It’s the difference between manual data aggregation and real-time intelligence.
Write back observations with structured FHIR bundles using create_observation.
Before this MCP, documenting a new blood pressure reading meant finding the 'Notes' section in the EHR and typing: 'BP 120/80 today.' This was unstructured text. It couldn't be graphed, trended against other readings, or used automatically by billing systems.
Now, using `create_observation`, your agent sends a structured FHIR bundle that contains specific fields for the date, time, measurement type (Blood Pressure), and the exact value. The data isn't just written; it’s saved in a machine-readable format that immediately enhances patient care quality.
What your AI can actually do with this
Managing healthcare interoperability shouldn't require deep knowledge of FHIR standards or wrestling with multiple API endpoints. This MCP lets your agent interact with clinical systems using natural language. You can search for patient records across connected health systems, pulling complete patient identifiers and demographics. Need context? Your agent retrieves active diagnoses and problem lists associated with a specific patient ID.
Want to document vitals? You write structured observations back into the EHR environment. Furthermore, you manage complex data exchanges by sending standardized Redox Data Model events. This capability is critical for any AI-driven medical assistant. If you're building something that needs reliable access to clinical context and has multiple endpoints, check out Vinkius; it hosts this MCP alongside thousands of others so you don’t have to connect everything separately.
019ea602-32ba-72a7-ab20-8e4001623152 Here's how it actually works
The bottom line is that your AI client handles all the complex API calling; you just ask it what you need.
First, subscribe to this MCP and provide your specific Redox API Key.
Next, point your AI client at the connection. You'll then interact with the tools using plain language prompts (e.g., 'Find active conditions for P123').
The agent executes the necessary calls in the background, retrieving or writing clinical data and presenting the results to you.
Who is this actually for?
This MCP targets developers and operational staff working in regulated healthcare environments. If your job involves accessing, validating, or entering patient data across different systems, this is for you. It solves the pain of having to switch between multiple dashboards just to piece together a single patient view.
You test clinical integrations and verify FHIR resource mapping directly from your coding environment, accelerating complex medical software development.
You quickly look up patient conditions or identifiers across disparate systems without navigating complex EHR user interfaces. You need immediate context on a patient's history.
You accelerate the development of AI-driven medical assistants by providing them with real-time, structured access to clinical data for testing and deployment.
What Changes When You Connect
Get instant patient context: Instead of manually cross-referencing multiple systems, your agent pulls active diagnoses and conditions using search_condition right in the chat.
Automated Documentation: Write vitals or new observations back to the EHR directly. The create_observation tool handles the structured FHIR format for you.
System Compatibility: Need to talk to a system that doesn't use standard FHIR? Use post_data_model to send notifications via standardized Redox Data Models.
Targeted Searching: Quickly locate a patient record using any available identifier. The search_patient tool eliminates the need for deep navigation through complex directory trees.
FHIR Compliance: Build trust in your applications knowing every interaction—from searching to writing back—adheres to industry standards.
See it in action
A triage nurse needs a quick patient overview.
The nurse's AI agent is prompted: 'Show me the current status and active diagnoses for MRN 54321.' The agent uses search_patient first, then calls search_condition, presenting the complete clinical profile without the nurse having to click through multiple tabs in the EHR.
A remote monitoring device needs to log a reading.
Instead of requiring a human to manually enter blood pressure readings, the IoT platform's agent uses create_observation to send structured FHIR vital signs directly into the patient’s record at the time of measurement.
A developer needs to test an integration endpoint.
The dev team wants to verify if a new department notification works across all connected systems. They use post_data_model to send a standardized event, confirming the system can process the payload correctly before deployment.
A care coordinator needs to find a patient from an external source.
The coordinator has only a partial identifier. They prompt their agent: 'Search for patients using this temporary ID.' The agent uses search_patient and provides the full, validated record details needed for follow-up.
The honest tradeoffs
Assuming all systems are identical.
Trying to send a custom JSON payload directly into a general endpoint without knowing the required schema. The system rejects it because it doesn't match FHIR or Redox standards.
Use post_data_model instead. This tool ensures your notification adheres to standardized Redox Data Models, making it compatible with a wider range of systems.
Only looking up demographics.
The agent only runs a basic name search on the patient directory and returns nothing useful because the system needs a specific identifier (like an MRN) to confirm identity.
Always start with search_patient using a known, validated identifier. This ensures you're pulling the correct, complete resource.
Manually documenting vitals in a notes field.
A user writes 'BP 120/80 on Tuesday.' This is unstructured text that clinicians can’t easily use for charting or trend analysis. The data loses its clinical utility.
Use create_observation. This tool forces the vitals into a structured FHIR bundle, making the data searchable and quantifiable by other systems.
When It Fits, When It Doesn't
Use this MCP if your primary need is to read or write standardized clinical information (diagnoses, vital signs) that requires adherence to industry standards like FHIR. You need more than just a simple lookup; you need the context of interconnected, structured data.
Don't use it if: 1) You only need basic contact information for an active directory (a simpler user service might suffice). 2) The system you are connecting to is extremely niche and uses proprietary, non-standardized JSON formats. If your needs fall outside standardized patient/condition data, this MCP won't help.
If you need to search by ID, use search_patient. If you need the list of active problems, use search_condition. If you are documenting something new, use create_observation.
Questions you might have
How does Redox MCP help with FHIR standards? +
It handles the complexity of FHIR compliance for you. Instead of needing to know how to structure a FHIR resource, you just tell your agent what data you need (e.g., 'Get active conditions'), and it manages the protocol.
Can I use Redox MCP if I don't have an MRN? +
Yes. You can still search by other identifiers like destination slugs or specific patient IDs, as long as those identifiers are recognized within your connected systems.
What is the difference between `search_patient` and searching in a normal EHR UI? +
search_patient allows your agent to query multiple, disparate healthcare sources simultaneously. It's an automated search layer built on top of existing systems, not just a single directory lookup.
Is `post_data_model` for sending general notifications? +
Yes. This tool lets you send standardized events (like 'New Appointment' or 'Test Result Ready') that multiple different backend systems can understand, regardless of their internal API.
Does Redox MCP only work in development mode? +
No. The MCP supports switching between environments for testing and deployment. You manage your environment directly within the connection settings to ensure you're querying the correct data source.
We've already built the connector for Redox. Just plug in your AI agents and start using Vinkius.
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