Heimdall MCP Server provides long-term memory for AI coding assistants. It benefits developers and operations teams by maintaining context across sessions. It connects to Claude-based agents and integrates with existing development workflows.
git clone https://github.com/lcbcFoo/heimdall-mcp-server.gitHeimdall MCP Server provides long-term memory for AI coding assistants. It benefits developers and operations teams by maintaining context across sessions. It connects to Claude-based agents and integrates with existing development workflows.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/lcbcFoo/heimdall-mcp-serverCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a coding assistant with long-term memory. Remember that I'm a developer at [COMPANY] working on [PROJECT]. Here's some context about our [INDUSTRY] application: [DATA]. Use this information in all future interactions. First, help me [TASK].
# Context Understanding I've stored the following context about your project: - Company: TechSolutions Inc. - Project: Cloud Migration Tool - Industry: SaaS - Key Data: - Current infrastructure: AWS - Target infrastructure: Multi-cloud - Key dependencies: Kubernetes, Terraform - Team size: 5 developers # Task Assistance For your first task, here's how we can approach the cloud migration: 1. **Assessment Phase**: - Review current AWS architecture - Identify cloud-agnostic components - Document potential multi-cloud benefits 2. **Design Phase**: - Propose Kubernetes cluster design - Suggest Terraform modules for multi-cloud 3. **Implementation Plan**: - Phase 1: Lift-and-shift to new cloud - Phase 2: Refactor for cloud-agnostic Would you like to dive deeper into any of these phases?
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