# GRACE Score Calculator MCP MCP > GRACE Score Calculator provides a standardized risk assessment for patients with Acute Coronary Syndrome (ACS). It takes nine core clinical inputs—like age, heart rate, and lab results—to calculate a composite score. This process predicts both immediate in-hospital mortality and long-term six-month prognosis, giving care teams an instant view of the patient's overall risk. ## Overview - **Category:** cardiology - **Price:** Free - **Tags:** GRACE Score, Acute Coronary Syndrome, Mortality Prediction, Risk Stratification, Cardiology ## Description Figuring out how sick a patient is with ACS can't just rely on checking vitals; you need a full risk picture. This MCP handles that complexity by standardizing the assessment process into one place. You feed it core clinical data—things like blood pressure, creatinine levels, and cardiac arrest status—and it runs through established medical guidelines to spit out a precise, weighted score. After generating the raw number, the system interprets that score, mapping it to an actionable risk category (Low, Intermediate, or High). We know patient care needs perfect accountability; that's why all calculations pass through Vinkius, ensuring every single data point and tool invocation creates a cryptographically signed audit trail. This means you always have a tamper-proof record of exactly how the final prognosis was determined, which is critical for clinical review. ## Tools ### calculate_grace_score This tool calculates the composite GRACE risk score when given a full set of clinical vital signs and lab results for an ACS patient. ### get_risk_category This tool takes a calculated GRACE score and maps it to an official risk category, providing actionable recommendations and mortality estimates. ## Prompt Examples **Prompt:** ``` Calculate the GRACE score for a 65-year-old male with HR of 90, SBP of 120, creatinine of 1.1, Killip Class II, no cardiac arrest, ST deviation of 1, and troponin elevated. ``` **Response:** ``` First, I will call `calculate_grace_score` with the provided vitals. Then, using the resulting score, I will call `get_risk_category` to get the final risk assessment and mortality estimates. ``` **Prompt:** ``` I need an estimated 6-month mortality risk for a patient with a calculated GRACE score of 78, who is age 75 and Killip Class III. ``` **Response:** ``` To determine the long-term prognosis, I will use `get_risk_category` (or a related prediction endpoint if available) with the score of 78. This function is designed to provide mortality estimates based on age and Killip class. ``` **Prompt:** ``` What are the critical parameters for the calculator? ``` **Response:** ``` The core calculation relies on inputting vitals like SBP, HR, and lab results (creatinine/troponin) via `calculate_grace_score` to generate the initial score. ``` ## Capabilities ### Calculate core risk score It computes the weighted GRACE score using nine specific physiological and lab inputs. ### Determine action-based risk level The MCP translates a raw numerical score into an easy-to-understand risk category (Low, Intermediate, or High) with clinical recommendations. ### Predict mortality probability It estimates the chances of death for both in-hospital stays and six months post-event using established lookups. ## Use Cases ### Triage in a high-volume ER setting A physician needs to quickly assess 15 incoming ACS patients. Instead of spending hours manually calculating each person's risk on paper, they feed the data into the agent. The system uses `calculate_grace_score` first, then immediately runs `get_risk_category`, sorting the list by highest predicted mortality risk for rapid triage. ### Longitudinal patient care planning A cardiologist is reviewing a high-risk patient six months post-discharge. They input the current vitals and use `get_risk_category` to determine if the initial treatment plan needs adjustment based on updated prognosis estimates. ### Academic research comparison A researcher needs to compare risk scores across three different hospital cohorts. They run `calculate_grace_score` against the same nine parameters for all groups, ensuring a single, standardized metric can be used for statistical analysis. ### Initial intake assessment A nurse enters initial vitals for a new patient suspecting ACS. The agent automatically calls `calculate_grace_score` to establish the baseline risk number right when the data is entered, preventing delays in critical care decisions. ## Benefits - Standardized assessment: Instead of relying on institution-specific guidelines, the `calculate_grace_score` uses a single, validated formula for all patients. - Full prognosis view: You don't just get one number. Using `get_risk_category`, you immediately see estimated mortality chances for both in-hospital stays and six months out. - Auditability: Every calculation is recorded with a cryptographically signed trail. This means the data pathway is completely transparent, which matters when making life-or-death decisions. - Input completeness: The system requires nine specific clinical parameters (age through troponin), forcing consistency that manual charting often misses. - Efficiency: By running both `calculate_grace_score` and `get_risk_category` in sequence, you cut down on the time spent cross-referencing risk charts. ## How It Works The bottom line is you get a standardized, multi-layered prognosis—not just a number, but an actionable category with long-term predictions attached. 1. Input all necessary patient data, including age, heart rate, systolic blood pressure, creatinine level, Killip class, cardiac arrest status, ST deviation, and troponin concentration. 2. The first step runs the `calculate_grace_score` to generate a weighted risk number based on established medical formulas. 3. Next, using that score, it calls `get_risk_category` which interprets the output, assigning a final risk level and providing mortality estimates. ## Frequently Asked Questions **What are the inputs required for calculate_grace_score?** You must provide nine specific clinical parameters: age, heart rate, systolic blood pressure, creatinine level, Killip class, cardiac arrest status, ST deviation, and troponin concentration. **Do I need to run calculate_grace_score before get_risk_category?** Yes. You must first use `calculate_grace_score` with the vitals data to generate the raw number; this score is then passed into `get_risk_category` for final interpretation. **What does get_risk_category tell me beyond just a risk level?** It provides more than just Low, Intermediate, or High. It gives you concrete mortality estimates for both in-hospital and six-month survival chances. **Can I calculate the GRACE score without lab results like troponin?** The system requires all nine core inputs listed in the documentation to run `calculate_grace_score` accurately. Missing any data point will prevent a complete calculation. **When I use `calculate_grace_score`, how is my patient data handled?** Your data passes through a zero-trust proxy. Credentials are used only during transit, never stored on disk. This ensures your keys remain secure across all calls to this MCP. **What happens if I give bad or incomplete data when running `calculate_grace_score`?** The tool validates inputs against established medical guidelines. If required fields are missing, it doesn't fail; instead, it returns a specific error code that tells you exactly which parameter needs correction. **Can I automate the full assessment using both `calculate_grace_score` and `get_risk_category`?** Yes. Your AI agent can chain them together in a single workflow. First, run `calculate_grace_score`. Then, feed that resulting score into `get_risk_category` to get a full risk profile automatically. **Is the use of `get_risk_category` performance-intensive or slow?** The MCP is built for speed and efficiency. It processes the generated score quickly, providing actionable risk categories instantly without creating noticeable latency in your workflow.