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Run real-time code analysis with bug, smell, and security fix suggestions.
The available materials indicate an open-source MIT MCP tool with no required secrets and no declared remote endpoints, with no clear high-risk red flags. The main concerns are its local execution and likely access to project source code, plus low community adoption and unknown maintenance status, so it is best used in a constrained environment.
The materials explicitly state that no keys or environment variables are required, and no API tokens, account credentials, or third-party authorizations are mentioned, so credential exposure and abuse risk appears low.
No remote endpoints or external service connections are declared, and the materials do not indicate that code or user data is sent out; based on the available information, network egress risk appears low.
The system checks indicate that this tool executes code or starts local processes; given its MCP server and code analysis purpose, it is expected to run analysis logic locally. This is a normal capability for this type of tool, and there is no current evidence of privileges or execution behavior beyond its stated function.
To provide real-time analysis and automated fixes for JavaScript, TypeScript, and Python, the tool will likely need to read project source files and may modify relevant files. The available materials do not show access to data unrelated to code analysis, but it should still be used under least-privilege principles.
Positive factors include that it is open source and MIT-licensed, making source review possible; however, it comes from a third-party registry, has 0 stars, unknown maintenance status, and no README content provided, so community validation and ongoing maintenance evidence are weak, warranting caution around supply-chain and dependency-update risk.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "SonarLint MCP Server" yet — see the docs or source repo.
Use SonarLint to analyze this Python code, identify potential vulnerabilities, code smells, and improvement points, and provide directly applicable fixes:
python
import subprocess
cmd = input('Command: ')
subprocess.run(cmd, shell=True)
Returns security findings, issue locations, severity levels, and safer replacement code to avoid shell injection.
Use SonarLint to analyze this TypeScript code for bug risks and code smells, then provide a cleaner refactored version:
ts
function format(items:any){
let result = '';
for (var i = 0; i < items.length; i++) {
result = result + items[i].name;
}
return result;
}
Highlights weak typing, variable scope, and maintainability issues, then outputs safer and cleaner refactored code.
Use SonarLint to run a pre-commit review on this JavaScript snippet. List bugs, readability issues, and auto-fixable items, then rank them by priority:
javascript
const user = {};
if(user.name == null){
console.log('No name')
}
setTimeout("alert('done')", 1000)
Provides a prioritized issue list, explanations, and quick-fix suggestions or replacement code.
Run local static analysis for TypeScript, JavaScript, and C# repositories.
Connect Sonar models for real-time web search and answers in MCP clients.
Query SonarQube metrics, issues, and duplication reports for faster code quality reviews.
Connect AI agents to SonarQube for code quality and security analysis.
Perform static taint analysis on Python code to detect security vulnerabilities.
Expose SonarQube quality data to AI for querying issues, rules, and gates.