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

Quick Start

Install

npm install -g mindkeg-mcp

Create an API key

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

Connect your AI agent

Mind Keg works with any MCP-compatible AI coding agent. Choose your setup:

Claude Code (stdio)

Add to ~/.claude.json or your project's MCP settings:

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

Cursor

Add to your Cursor MCP settings (.cursor/mcp.json or global settings):

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

Windsurf

Add to your Windsurf MCP configuration (~/.codeium/windsurf/mcp_config.json):

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

HTTP mode (any MCP client)

For agents that connect via HTTP instead of stdio:

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"
      }
    }
  }
}

Other MCP-compatible agents

Mind Keg works with any agent that supports the Model Context Protocol — including Codex CLI, Gemini CLI, GitHub Copilot, and more. Use the stdio config above adapted to your agent's MCP settings format.

Add Mind Keg instructions to your repository

Copy templates/AGENTS.md to the root of any repository where you want agents to use Mind Keg.

AGENTS.md is the industry standard supported by 20+ AI tools (Cursor, Windsurf, Codex, Gemini CLI, GitHub Copilot, etc.).

Claude Code only: Claude Code doesn't auto-load AGENTS.md natively. Add @AGENTS.md to your CLAUDE.md to bridge it.

MCP Tools

CLI Commands

# Start in stdio mode (for local agent connections)
mindkeg serve --stdio

# Start in HTTP mode (for remote connections)
mindkeg serve --http

# API key management
mindkeg api-key create --name "My Key"
mindkeg api-key create --name "Team Key" --repositories /repo/a /repo/b
mindkeg api-key list
mindkeg api-key revoke <prefix>

# Database
mindkeg migrate

# Backup and restore
mindkeg export --output backup.json
mindkeg import backup.json --regenerate-embeddings

Configuration

Embedding providers

FastEmbed (default, free, local)

Semantic search works out of the box using FastEmbed — no API key needed, no network calls. Uses BAAI/bge-small-en-v1.5 (384 dimensions) via local ONNX Runtime. Model files are downloaded once on first use (~50MB).

OpenAI (paid, best quality)

export MINDKEG_EMBEDDING_PROVIDER=openai
export OPENAI_API_KEY=sk-...

Uses text-embedding-3-small (1536 dimensions). Best semantic search quality but requires an API key and incurs per-request costs.

None (keyword search only)

export MINDKEG_EMBEDDING_PROVIDER=none

Disables semantic search and falls back to SQLite FTS5 full-text search — all other features work identically.

Data Model

Each learning contains:

Scoping

Learnings have three scope levels:

Workspaces are auto-detected from the parent folder of a repository path. For example, if your repos are organized as:

repositories/
  personal/     ← workspace
    app-a/
    app-b/
  work/          ← workspace
    project-x/

A workspace learning stored under repositories/personal/ is shared across app-a and app-b but not project-x.

When searching, results include all three scopes: repo-specific + workspace + global. Each result has a scope field indicating its level.

What Makes a Good Learning?

• Atomic: One insight per entry. Max 500 characters.

• Actionable: What to DO or AVOID, not just what exists.

• Specific: Mentions the concrete context (library, pattern, file).

Good: "Always wrap Prisma queries in try/catch — it throws on constraint violations, not returns null."

Bad: "Be careful with the database." (too vague)

Development

# Clone and install
git clone ...
npm install

# Run tests
npm test

# Build
npm run build

# Development mode (rebuilds on change)
npm run dev

# Type check
npm run typecheck

Running without external APIs

Mind Keg works fully offline by default. FastEmbed provides free, local semantic search using ONNX Runtime — no API keys or network calls required. All CRUD operations and search work out of the box.

Architecture

CLI (Commander.js)
  └── serve / api-key / migrate / export / import

src/
  index.ts          Entry point, stdio + HTTP transports
  server.ts         MCP server + tool registration
  config.ts         Config loading (env vars → defaults)
  auth/             API key generation + validation middleware
  tools/            One file per MCP tool (8 tools)
  services/         LearningService + EmbeddingService
  storage/          StorageAdapter interface + SQLite impl
  models/           Zod schemas + TypeScript types
  utils/            Logger (pino → stderr) + error classes

templates/
  AGENTS.md         Template for instructing agents to use Mind Keg

See CLAUDE.md for detailed development conventions.

License

MIT