---

name: ide-diagnostics description: VS Code language diagnostics and Jupyter code execution using IDE MCP. Use for checking type errors, running Python code in notebooks, or getting real-time code analysis. Triggers on requests for diagnostics, type checking, or notebook execution.

IDE Integration

VS Code integration for language diagnostics and Jupyter notebook execution.

Available Tools

getDiagnostics

Get language server diagnostics (errors, warnings, hints) from VS Code.

Copy# Get all diagnostics
result = mcp__ide__getDiagnostics()

# Get diagnostics for specific file
result = mcp__ide__getDiagnostics(uri="file:///path/to/file.ts")

Response Format

Copy{
    "diagnostics": {
        "file:///path/to/file.ts": [
            {
                "severity": "error",
                "message": "Type 'string' is not assignable to type 'number'",
                "range": {
                    "start": {"line": 10, "character": 5},
                    "end": {"line": 10, "character": 15}
                },
                "source": "typescript",
                "code": 2322
            }
        ]
    }
}

Severity Levels

executeCode

Execute Python code in the current Jupyter kernel.

Copy# Execute Python code
result = mcp__ide__executeCode(code="print('Hello World')")

# Multi-line code
result = mcp__ide__executeCode(code="""
import pandas as pd
df = pd.read_csv('data.csv')
print(df.head())
""")

Response Format

Copy{
    "outputs": [
        {
            "type": "text",  # or "image", "error", "stream"
            "content": "Hello World",
            "mimeType": "text/plain"
        }
    ],
    "executionCount": 1
}

Output Types

Common Use Cases

Pre-commit Type Check

Copy# Check for errors before committing
diagnostics = mcp__ide__getDiagnostics()

errors = []
for file, issues in diagnostics["diagnostics"].items():
    file_errors = [i for i in issues if i["severity"] == "error"]
    if file_errors:
        errors.extend(file_errors)

if errors:
    print(f"Found {len(errors)} errors - fix before committing")

Data Analysis in Notebook

Copy# Load and explore data
mcp__ide__executeCode(code="""
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('sales.csv')
print(df.describe())
""")

# Create visualization
mcp__ide__executeCode(code="""
df.groupby('month')['revenue'].sum().plot(kind='bar')
plt.title('Monthly Revenue')
plt.show()
""")

Variable Inspection

Copy# Check variable state
mcp__ide__executeCode(code="print(type(my_var), repr(my_var))")

# Inspect DataFrame
mcp__ide__executeCode(code="display(df.info())")

Important Notes

1. Kernel state persists - Variables remain between executeCode calls
2. Restart clears state - Kernel restart removes all variables
3. Diagnostics are real-time - Reflect current file state
4. File URI format - Use file:///absolute/path for specific files

Best Practices

1. Check diagnostics before commits - Catch errors early
2. Use executeCode for exploration - Interactive data analysis
3. Preserve kernel state - Avoid unnecessary recomputation
4. Handle errors gracefully - Check output type for errors