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# Socket MCP Server
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A Model Context Protocol (MCP) server for Socket integration, allowing AI assistants to efficiently check dependency vulnerability scores and security information.
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A Model Context Protocol (MCP) server for Socket integration, allowing AI assistants to efficiently check dependency vulnerability scores and security information.
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🛠️ This project is in early development and rapidly evolving.
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1. Install the Socket MCP server:
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```bash
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npm install -g socket-mcp
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npm install -g @socketsecurity/mcp
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```
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2. Set the API key in your environment:
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```
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3. Now you can ask questions about dependency security within your VS Code environment.
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4. Feel free to add the following to your `.github/copilot-instructions.md` file to control better how the tools is used by your agent:
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4. Feel free to add the following to your `.github/copilot-instructions.md` file to control better how the tools is used by your agent:
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```markdown
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## Socket MCP Server
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## Adjust tool usage with custom rules
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You can further customize how the Socket MCP server interacts with your AI assistant by modifying your client rules. The rules are usually a markdown file and its location depends on the AI assistant you are using.
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You can further customize how the Socket MCP server interacts with your AI assistant by modifying your client rules. The rules are usually a markdown file and its location depends on the AI assistant you are using.
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| MCP Client | Rules File Location |
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|---|---|
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Always check dependency scores with the debscore tool when you add a new dependency. If the score is low, consider using an alternative library or writing the code yourself. If you are unsure about the score, ask for a review from someone with more experience. When checking dependencies, make sure to also check the imports not just the pyproject.toml file.
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```
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You can adjust the rules to fit your needs. For example, you can add rules to include specific manifest files, or guide the AI assistant on how to handle low scores. The rules are flexible and can be tailored to your workflow.
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You can adjust the rules to fit your needs. For example, you can add rules to include specific manifest files, or guide the AI assistant on how to handle low scores. The rules are flexible and can be tailored to your workflow.
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