Mind Keg MCP Server

Mind Keg MCP

A persistent memory MCP server for AI coding agents. Stores atomic learnings — debugging insights, architectural decisions, codebase conventions — so every agent session starts with relevant institutional knowledge.

Problem

AI coding agents (Claude Code, Cursor, Windsurf) lose context between sessions. Hard-won insights are forgotten the moment a conversation ends. Developers repeatedly re-explain the same things; agents repeatedly make the same mistakes.

Mind Keg solves this with a centralized, persistent brain that any MCP-compatible agent can query and contribute to.

How It Works

Mind Keg implements a RAG (Retrieval-Augmented Generation) pattern for AI coding agents:

1. Retrieval — Agent searches the brain for relevant learnings using semantic or keyword search
2. Augmentation — Retrieved learnings are injected into the agent's conversation context
3. Generation — The agent responds with awareness of past discoveries and decisions

Unlike traditional RAG systems that chunk large documents, Mind Keg stores pre-curated atomic learnings (max 500 chars each). No chunking strategy needed — each learning IS the retrieval unit. The agent controls both retrieval and storage, creating a feedback loop where knowledge improves over time.

Features

• Store and retrieve atomic learnings (max 500 chars, one insight per entry)
• Semantic search with three provider options:
  • FastEmbed (free, local, ONNX-based — BAAI/bge-small-en-v1.5, 384 dims)
  • OpenAI (paid, best quality — text-embedding-3-small, 1536 dims)
  • None (FTS5 keyword fallback — zero external dependencies)
• Six categories: architecture, conventions, debugging, gotchas, dependencies, decisions
• Free-form tags and group linking
• Three scoping levels: repository-specific, workspace-wide, and global learnings
• Dual transport: stdio (local) + HTTP+SSE (remote)
• API key authentication with per-repository access control
• SQLite storage (zero dependencies, zero config)
• Import/export for backup and migration
• Enterprise security: encryption at rest, audit logging, TTL/data retention, Prometheus monitoring, rate limiting, content integrity verification

Quick Start

One-command setup

npx mindkeg-mcp init

This auto-detects your agent (Claude Code, Cursor, Windsurf), writes the MCP config, copies agent instructions, and runs a health check. That's it — open your agent and start coding.

Options:

npx mindkeg-mcp init --agent cursor      # Target a specific agent
npx mindkeg-mcp init --no-instructions   # Skip copying AGENTS.md
npx mindkeg-mcp init --no-health-check   # Skip the health check

init is idempotent — safe to run multiple times. It merges with existing configs and never overwrites.

Manual setup

If you prefer to configure manually, or need HTTP mode:

npm install -g mindkeg-mcp

mindkeg api-key create --name "My Laptop"
# Displays the key ONCE — save it securely
# mk_abc123...

{
  "mcpServers": {
    "mindkeg": {
      "command": "mindkeg",
      "args": ["serve", "--stdio"],
      "env": {
        "MINDKEG_API_KEY": "mk_your_key_here"
      }
    }
  }
}

{
  "mcpServers": {
    "mindkeg": {
      "command": "mindkeg",
      "args": ["serve", "--stdio"],
      "env": {
        "MINDKEG_API_KEY": "mk_your_key_here"
      }
    }
  }
}

{
  "mcpServers": {
    "mindkeg": {
      "command": "mindkeg",
      "args": ["serve", "--stdio"],
      "env": {
        "MINDKEG_API_KEY": "mk_your_key_here"
      }
    }
  }
}

MINDKEG_API_KEY=mk_your_key mindkeg serve --http
# Listening on http://127.0.0.1:52100/mcp

{
  "mcpServers": {
    "mindkeg": {
      "type": "http",
      "url": "http://127.0.0.1:52100/mcp",
      "headers": {
        "Authorization": "Bearer mk_your_key_here"
      }
    }
  }
}