GRACE Score Calculator MCP for AI. Predict patient risk for ACS with core vitals.
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The GRACE Score Calculator determines a patient's acute coronary syndrome risk using nine clinical inputs. It computes a weighted score and maps that number to immediate, actionable risk categories.
This helps predict both short-term in-hospital survival chances and long-term mortality probability for care teams.
What your AI can do
Calculate grace score
Computes the foundational GRACE risk score using a patient's vitals and clinical parameters.
Get risk category
Maps the resulting numerical score to an actionable, clinically defined risk category and recommendation.
Calculates the foundational GRACE numerical score by processing a patient's vitals and lab results.
Assigns an actionable clinical category (Low, Intermediate, or High) based on the calculated score.
Predicts a patient's probability of surviving the current hospital stay.
Provides an estimate of 6-month mortality risk based on established clinical metrics.
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GRACE Score Calculator: 2 Tools
Use these two tools to calculate a patient's foundational GRACE score and then translate that number into an immediate, actionable risk classification.
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 GRACE Score Calculator on VinkiusCalculate Grace Score
Computes the foundational GRACE risk score using a patient's vitals and clinical parameters.
Get Risk Category
Maps the resulting numerical score to an actionable, clinically defined risk...
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
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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 connection provides 2 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manual Risk Assessment: The Pitfalls of Paperwork
Today, assessing ACS risk often means manually gathering age, SBP, HR readings, and lab results from multiple patient charts. You then have to cross-reference these numbers against various institutional guidelines—one for in-hospital care, another for six months out. This is tedious; it involves checking boxes, calculating weighted averages by hand, and praying you don't misread a vital sign or use the wrong formula.
With this MCP, your agent takes all those inputs, feeds them into the standardized calculation, and returns the full risk picture instantly. You get an objective score that dictates immediate next steps, eliminating human error and wasted time.
Get a Clear Risk Profile with GRACE Score Calculator
The process of calculating the raw number is handled by `calculate_grace_score`. Then, instead of having to manually interpret what that score means—is it 'medium' or 'high'?—you simply run `get_risk_category` against the result. This function automatically translates the data point into a clear clinical mandate.
The difference is simple: you move from gathering numbers and making educated guesses, to receiving an authoritative, two-part risk assessment that guides your entire care plan.
What your AI can actually do with this
When a patient presents with signs of Acute Coronary Syndrome (ACS), clinicians can't rely on just looking at vital signs; they need a standardized way to assess overall risk. This MCP provides that structure, taking clinical data points like age, heart rate, blood pressure, and lab results to compute a reliable GRACE score.
The tool goes beyond the raw number, assigning an actionable risk level—Low, Intermediate, or High—and estimating both immediate in-hospital danger and long-term survival chances. You connect this MCP through Vinkius to your AI client, giving your agent access to established medical guidelines for precise patient stratification. Instead of sifting through multiple protocols, you get a single, data-driven view that helps care teams prioritize resources immediately.
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Here's how it actually works
The bottom line is you get an immediate, comprehensive view of the patient's acute and long-term prognosis using standardized medical calculations.
Input nine core patient parameters, including age, heart rate, systolic blood pressure, and troponin levels.
The system first uses the raw vitals to compute a weighted composite GRACE risk score (0-100).
It then takes that score, maps it to a specific risk category, and estimates both in-hospital and six-month mortality probabilities.
Who is this actually for?
Cardiologists and ER physicians who need to quickly triage a patient presenting with ACS. Nurses in critical care units benefit when they need a reliable, objective way to prioritize resource allocation based on predicted severity.
Uses the MCP to validate initial risk assessments and determine if advanced intervention is needed for ACS patients.
Runs quick assessments on incoming patients to triage care and guide immediate treatment decisions under pressure.
Monitors patient vitals against the MCP output to alert the care team when a patient's risk level changes rapidly.
What Changes When You Connect
Standardized Assessment: Stop manual calculations. Use calculate_grace_score to generate a weighted score based on established guidelines, ensuring every patient is assessed identically.
Dual Prognosis View: Get more than just an immediate number. The MCP estimates both in-hospital and six-month mortality probabilities for complete care planning.
Immediate Triage Guidance: Use get_risk_category to instantly move past a raw score and get a clear, actionable risk designation (Low, Intermediate, or High).
Resource Prioritization: Care teams can use the comprehensive results to allocate staff and resources exactly where they're needed most.
Efficiency Over Time: The process cuts out time spent cross-referencing multiple clinical protocols, giving you a single source of truth for risk.
See it in action
Triage in the Emergency Department
An ER physician needs to rapidly assess an incoming patient with chest pain. They feed the vitals into the MCP, which first runs calculate_grace_score. The resulting score is immediately passed through get_risk_category, telling them whether this patient requires immediate cardiac intervention or if observation is sufficient.
Discharge Planning
A cardiologist needs to plan a follow-up for a stable ACS patient. They use the MCP to determine the long-term prognosis, ensuring the patient's risk score warrants specific lifestyle changes or follow-up testing before they leave the facility.
Consulting on Complex Cases
A clinical team reviews a patient with mixed symptoms. They input all nine vitals into calculate_grace_score to establish a baseline score, then use get_risk_category to provide the necessary context for their treatment plan.
Comparing Treatment Options
Before approving invasive procedures, a physician runs two scenarios through the MCP. By comparing the risk category outputs from both treatments, they can make an informed decision based on predicted patient outcomes.
The honest tradeoffs
Relying only on vitals
A clinician looks at a heart rate and blood pressure reading but ignores the lab results or age.
Don't stop there. You must input all nine required parameters into calculate_grace_score to get the full, weighted score.
Ignoring risk classification
The agent returns a raw score like '72,' and the user is left guessing what that means for care.
Always follow up by running get_risk_category using the calculated score. This translates the number into an immediate Low, Intermediate, or High action level.
Manually cross-referencing guidelines
The team spends minutes checking multiple printed protocols to see if the patient's condition meets high-risk criteria.
Let your agent do the work. Using both calculate_grace_score and get_risk_category automates the comparison against established medical standards.
When It Fits, When It Doesn't
Use this MCP if you need a standardized, quantitative way to assess risk in acute cardiac events. It's ideal for situations where initial vitals alone aren't enough and you require both a raw score calculation and an immediate, actionable risk classification.
Don't use it if your goal is simply data logging or tracking historical records; there are simpler database tools for that. Also, remember this tool gives a prediction based on current data—it does not replace the final clinical diagnosis from a physician. If you need to calculate scores for entirely different cardiac conditions (e.g., pulmonary embolism), use a specialized risk calculator MCP instead.
Questions you might have
What kind of patient data does calculate_grace_score need? +
It requires nine core clinical parameters, including age, heart rate (HR), systolic blood pressure (SBP), creatinine level, Killip class, cardiac arrest status, ST deviation, and troponin concentration.
Does the GRACE Score Calculator predict only short-term outcomes? +
No. The tool calculates both immediate in-hospital mortality probabilities and estimates for 6-month mortality risk, providing a comprehensive view of prognosis.
How do I interpret the output from get_risk_category? +
The function maps the score to an explicit category (Low, Intermediate, or High) and provides clinical recommendations for that specific level of risk.
Is this useful if my patient is stable? +
Yes. Even for stable patients, running the assessment helps define a baseline risk profile. It guides necessary follow-up care or preventative resource allocation.
If I use `calculate_grace_score` and input data that falls outside the standard clinical range, what error message do I receive? +
The system will return a specific validation failure. Before calculating the score, it checks inputs like age or blood pressure against established medical norms to ensure data integrity.
After running `calculate_grace_score`, how do I get actionable clinical recommendations? +
You must call get_risk_category next. This second function takes the raw score and maps it to a defined risk level, providing immediate care suggestions based on that classification.
Can `calculate_grace_score` compute a risk estimate if I am missing one of the nine core clinical parameters? +
No. To maintain accuracy, the calculation requires all specified inputs—age, HR, SBP, creatinine, etc.—since the score uses a weighted model across every parameter.
Is patient data submitted to `calculate_grace_score` handled securely for clinical use? +
Yes. The API adheres to strict guidelines for managing Protected Health Information (PHI). All submissions are encrypted and processed using industry-standard security protocols.
What inputs are required for the GRACE score calculation? +
The calculate_grace_score tool requires nine key parameters: age, heart rate, systolic blood pressure, potassium, creatinine, Killip class, cardiac arrest status, ST deviation, and enzyme elevation. Providing accurate data for all fields is critical for an accurate score.
Does the tool provide more than just a numerical score? +
No. The system uses get_risk_category to translate the raw score into an actionable risk category (Low, Intermediate, High), providing immediate clinical recommendations and estimated mortality ranges for both hospital stay and six months post-discharge.
Is this tool suitable for all patients with chest pain? +
The GRACE score is designed specifically for Acute Coronary Syndrome (ACS) patients. While the calculate_grace_score tool can accept general vital signs, clinical interpretation must be done by a qualified medical professional.
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