enowx-rag

Per-project RAG memory MCP server. Each project gets its own vector collection, so an LLM can index context about a codebase and retrieve it quickly.

Quick setup (copy-paste this to your AI agent)

Copy the prompt below and paste it into Claude Code, Cline, Cursor, OpenCode, Codex, Factory Droid, Roo, Zed, Windsurf, or Continue:

I want to set up enowx-rag, a per-project RAG memory MCP server.

Read the setup guide at https://raw.githubusercontent.com/enowdev/enowx-rag/main/README.md and the skill at https://raw.githubusercontent.com/enowdev/enowx-rag/main/skill/enowx-rag.md

Then follow these steps:
1. Clone the repo to my machine: git clone https://github.com/enowdev/enowx-rag.git
2. Build the MCP server: cd enowx-rag/mcp-server && go build ./cmd/mcp-server
3. Ask me which embedding option I prefer:
   - **Voyage AI** (recommended): no local setup needed, ask for my Voyage API key (free at voyageai.com, 200M free tokens with voyage-4).
   - **Self-hosted TEI**: start Qdrant + TEI with docker compose. Ask for Qdrant URL if I already have one running.
4. Install the MCP server in every coding tool I use. Use the correct config format per tool (the README has all formats from official docs).
5. For the project I'm currently working in, create or merge AGENTS.md and CLAUDE.md with RAG memory instructions. Use project ID derived from the folder name. Call rag_create_project to create the collection.
6. Do not replace existing AGENTS.md or CLAUDE.md. Append the RAG section with a --- separator.

What agents should do

Install the Factory Droid skill.
Build the Go MCP server.
Start the RAG backend (if local mode).
Install the MCP server in every coding tool the user uses.
Generate AGENTS.md and CLAUDE.md in the target project so all agents automatically use the RAG memory.

1. Install the Factory Droid skill

Copy the skill file into the Droid skill directory:

mkdir -p ~/.factory/skills/enowx-rag
cp /Users/enowdev/Project/enowx-rag/skill/enowx-rag.md ~/.factory/skills/enowx-rag/skill.md

If the skill must be reloaded, restart the Droid session or refresh skills.

2. Build the MCP server

cd /Users/enowdev/Project/enowx-rag/mcp-server
go build ./cmd/mcp-server

This produces the binary /Users/enowdev/Project/enowx-rag/mcp-server/mcp-server.

3. Start the RAG backend (local mode)

If the user does not already have a Qdrant + TEI backend, start the local one:

cd /Users/enowdev/Project/enowx-rag/mcp-server
docker compose up -d qdrant tei-embedding

Verify:

curl -f http://localhost:6333/healthz
curl -f http://localhost:8081/health

If using an existing backend, skip this step and set the correct URLs in the tool config below.

4. Install the MCP server in every coding tool

Each tool has a different config format and file location. Use the correct one per tool.

Quick reference table

Tool	Format	Key	Config file
Claude Code	JSON	`mcpServers`	`~/.claude.json` or `.mcp.json` in project root
Claude Desktop	JSON	`mcpServers`	`~/Library/Application Support/Claude/claude_desktop_config.json`
Cline	JSON	`mcpServers`	`~/.cline/mcp.json` or IDE MCP settings
Cursor	JSON	`mcpServers`	`~/.cursor/mcp.json` or `.cursor/mcp.json` in project root
OpenCode	JSON	`mcp`	`~/.opencode/settings.json` or `opencode.json` in project root
Codex (OpenAI)	TOML	`[mcp_servers.*]`	`~/.codex/config.toml`
Factory Droid	CLI	—	`droid mcp add enowx-rag /path/to/mcp-server`
Roo Code	JSON	`mcpServers`	global `mcp_settings.json` or `.roo/mcp.json` in project root
Zed	JSON	`context_servers`	`~/.config/zed/settings.json`
Windsurf	JSON	`mcpServers`	`~/.codeium/windsurf/mcp_config.json`
Continue	YAML	`mcpServers` (list)	`~/.continue/config.yaml`

Claude Code

CLI (Voyage AI):

claude mcp add --transport stdio enowx-rag \
  --env RAG_VECTOR_STORE=qdrant \
  --env RAG_QDRANT_URL=http://localhost:6333 \
  --env RAG_VOYAGE_API_KEY=your-voyage-api-key \
  -- /path/to/enowx-rag/mcp-server/mcp-server

CLI (self-hosted TEI):

claude mcp add --transport stdio enowx-rag \
  --env RAG_VECTOR_STORE=qdrant \
  --env RAG_QDRANT_URL=http://localhost:6333 \
  --env RAG_TEI_URL=http://localhost:8081 \
  -- /path/to/enowx-rag/mcp-server/mcp-server

JSON (~/.claude.json or .mcp.json) — Voyage AI:

{
  "mcpServers": {
    "enowx-rag": {
      "command": "/path/to/enowx-rag/mcp-server/mcp-server",
      "env": {
        "RAG_VECTOR_STORE": "qdrant",
        "RAG_QDRANT_URL": "http://localhost:6333",
        "RAG_VOYAGE_API_KEY": "your-voyage-api-key",
        "RAG_VOYAGE_MODEL": "voyage-4"
      }
    }
  }
}

JSON — self-hosted TEI:

{
  "mcpServers": {
    "enowx-rag": {
      "command": "/path/to/enowx-rag/mcp-server/mcp-server",
      "env": {
        "RAG_VECTOR_STORE": "qdrant",
        "RAG_QDRANT_URL": "http://localhost:6333",
        "RAG_TEI_URL": "http://localhost:8081"
      }
    }
  }
}

All examples below use Voyage AI (recommended). Replace RAG_VOYAGE_API_KEY with RAG_EMBEDDER=tei + RAG_TEI_URL if using self-hosted TEI.

Claude Desktop

File: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "enowx-rag": {
      "command": "/path/to/enowx-rag/mcp-server/mcp-server",
      "env": {
        "RAG_VECTOR_STORE": "qdrant",
        "RAG_QDRANT_URL": "http://localhost:6333",
        "RAG_VOYAGE_API_KEY": "your-voyage-api-key",
        "RAG_VOYAGE_MODEL": "voyage-4"
      }
    }
  }
}

Cline

File: ~/.cline/mcp.json (CLI) or open Cline panel > MCP Servers > Configure (IDE)

{
  "mcpServers": {
    "enowx-rag": {
      "command": "/path/to/enowx-rag/mcp-server/mcp-server",
      "env": {
        "RAG_VECTOR_STORE": "qdrant",
        "RAG_QDRANT_URL": "http://localhost:6333",
        "RAG_VOYAGE_API_KEY": "your-voyage-api-key",
        "RAG_VOYAGE_MODEL": "voyage-4"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Cursor

File: ~/.cursor/mcp.json (global) or .cursor/mcp.json (project)

{
  "mcpServers": {
    "enowx-rag": {
      "type": "stdio",
      "command": "/path/to/enowx-rag/mcp-server/mcp-server",
      "env": {
        "RAG_VECTOR_STORE": "qdrant",
        "RAG_QDRANT_URL": "http://localhost:6333",
        "RAG_VOYAGE_API_KEY": "your-voyage-api-key",
        "RAG_VOYAGE_MODEL": "voyage-4"
      }
    }
  }
}

OpenCode

File: ~/.opencode/settings.json or opencode.json in project root

OpenCode uses mcp key (not mcpServers), command as array, and environment (not env).

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "enowx-rag": {
      "type": "local",
      "command": ["/path/to/enowx-rag/mcp-server/mcp-server"],
      "enabled": true,
      "environment": {
        "RAG_VECTOR_STORE": "qdrant",
        "RAG_QDRANT_URL": "http://localhost:6333",
        "RAG_VOYAGE_API_KEY": "your-voyage-api-key",
        "RAG_VOYAGE_MODEL": "voyage-4"
      }
    }
  }
}

Codex (OpenAI)

File: ~/.codex/config.toml (TOML, not JSON)

CLI:

codex mcp add enowx-rag \
  --env RAG_VECTOR_STORE=qdrant \
  --env RAG_QDRANT_URL=http://localhost:6333 \
  --env RAG_VOYAGE_API_KEY=your-voyage-api-key \
  -- /path/to/enowx-rag/mcp-server/mcp-server

TOML:

[mcp_servers.enowx-rag]
command = "/path/to/enowx-rag/mcp-server/mcp-server"

[mcp_servers.enowx-rag.env]
RAG_VECTOR_STORE = "qdrant"
RAG_QDRANT_URL = "http://localhost:6333"
RAG_VOYAGE_API_KEY = "your-voyage-api-key"
RAG_VOYAGE_MODEL = "voyage-4"

Factory Droid

droid mcp add enowx-rag /path/to/enowx-rag/mcp-server/mcp-server

Roo Code

File: global mcp_settings.json (via Roo Code > MCP > Edit Global MCP) or .roo/mcp.json (project)

{
  "mcpServers": {
    "enowx-rag": {
      "command": "/path/to/enowx-rag/mcp-server/mcp-server",
      "env": {
        "RAG_VECTOR_STORE": "qdrant",
        "RAG_QDRANT_URL": "http://localhost:6333",
        "RAG_VOYAGE_API_KEY": "your-voyage-api-key",
        "RAG_VOYAGE_MODEL": "voyage-4"
      },
      "alwaysAllow": [],
      "disabled": false
    }
  }
}

Zed

File: ~/.config/zed/settings.json

Zed uses context_servers key (not mcpServers). Can also be added via UI: Command Palette > agent: add context server.

{
  "context_servers": {
    "enowx-rag": {
      "command": "/path/to/enowx-rag/mcp-server/mcp-server",
      "args": [],
      "env": {
        "RAG_VECTOR_STORE": "qdrant",
        "RAG_QDRANT_URL": "http://localhost:6333",
        "RAG_VOYAGE_API_KEY": "your-voyage-api-key",
        "RAG_VOYAGE_MODEL": "voyage-4"
      }
    }
  }
}

Windsurf

File: ~/.codeium/windsurf/mcp_config.json

Can also be added via MCP Marketplace UI in Windsurf.

{
  "mcpServers": {
    "enowx-rag": {
      "command": "/path/to/enowx-rag/mcp-server/mcp-server",
      "env": {
        "RAG_VECTOR_STORE": "qdrant",
        "RAG_QDRANT_URL": "http://localhost:6333",
        "RAG_VOYAGE_API_KEY": "your-voyage-api-key",
        "RAG_VOYAGE_MODEL": "voyage-4"
      }
    }
  }
}

Continue

File: ~/.continue/config.yaml (YAML, not JSON)

Continue uses mcpServers as a list of objects with name, command, env.

mcpServers:
  - name: enowx-rag
    command: /path/to/enowx-rag/mcp-server/mcp-server
    env:
      RAG_VECTOR_STORE: qdrant
      RAG_QDRANT_URL: http://localhost:6333
      RAG_VOYAGE_API_KEY: your-voyage-api-key
      RAG_VOYAGE_MODEL: voyage-4

Replace /path/to/enowx-rag/mcp-server/mcp-server with the actual absolute path to the built binary.

5. Enable RAG memory for the target project

Per-project collection

Each project gets its own isolated collection in the vector store: project_<PROJECT_ID>. Call rag_create_project with the project ID to create it. Multiple projects share the same MCP server and backend, but each has isolated memory.

AGENTS.md and CLAUDE.md

Create or update these two files in the root of the target project. Replace PROJECT_ID with the actual project name or slug.

Important: merge, do not replace. If the file already exists, append the RAG section below existing content separated by ---. Use the templates in skill/templates/AGENTS.md and skill/templates/CLAUDE.md.

`AGENTS.md`

# Project agent instructions

This project uses the `enowx-rag` MCP server for per-project memory.

## Before coding

1. Call `rag_retrieve_context` with the project ID `PROJECT_ID` and the user's query.
2. Read the returned context. If relevant, use it to shape your answer or plan.

## After coding

1. Summarize what you changed.
2. Call `rag_index` with useful new facts, design decisions, gotchas, or patterns under project ID `PROJECT_ID`.
3. Call `rag_index_project` with the project directory to sync all file changes into RAG. Always do this — it handles new files, edits, and deletions automatically.

Keep chunks concise (one idea per chunk). Use metadata tags like `type:architecture`, `type:decision`, `type:api`, `type:bugfix`, `type:howto`, or `type:snippet`.

`CLAUDE.md`

# Claude instructions for this project

## RAG memory workflow

### Before making significant changes

1. Call `rag_retrieve_context` with the project ID `PROJECT_ID` and the user's query.
2. Read the returned context. If it is empty or irrelevant, continue as normal.

### After completing work

1. Summarize what you changed and why.
2. Call `rag_index` with useful new facts, design decisions, gotchas, or patterns under project ID `PROJECT_ID`.

Use project ID: `PROJECT_ID`

Each project has its own collection: `project_PROJECT_ID`. Do not mix project memories.

Layout

enowx-rag/
├── AGENTS.md           # Universal agent install guide (this repo)
├── CLAUDE.md           # Claude-family quick reference (this repo)
├── README.md           # This file
├── mcp-server/         # Go MCP server (stdio transport)
│   ├── cmd/mcp-server
│   ├── pkg/rag         # Provider interface + Qdrant, Chroma, pgvector
│   ├── Dockerfile
│   └── docker-compose.yml
└── skill/              # Factory Droid skill
    ├── enowx-rag.md
    └── templates/
        └── AGENTS.md

Providers

Vector store	Embedder	Status
Qdrant	TEI (self-hosted)	Ready
Qdrant	Voyage AI	Ready
Chroma	TEI (self-hosted)	Ready
pgvector	TEI (self-hosted)	Ready

Embedding options

Option A: Voyage AI (recommended, hosted)

No GPU required. Get a free API key at voyageai.com. voyage-4 has 200M free tokens.

Set RAG_EMBEDDER=voyage (or just set RAG_VOYAGE_API_KEY — it auto-detects):

"env": {
  "RAG_VECTOR_STORE": "qdrant",
  "RAG_QDRANT_URL": "http://localhost:6333",
  "RAG_EMBEDDER": "voyage",
  "RAG_VOYAGE_API_KEY": "your-voyage-api-key",
  "RAG_VOYAGE_MODEL": "voyage-4"
}

Option B: TEI (self-hosted)

Runs a local Text Embeddings Inference container. Requires Docker and ~1 GB of RAM.

cd mcp-server && docker compose up -d qdrant tei-embedding

"env": {
  "RAG_VECTOR_STORE": "qdrant",
  "RAG_QDRANT_URL": "http://localhost:6333",
  "RAG_EMBEDDER": "tei",
  "RAG_TEI_URL": "http://localhost:8081"
}

Environment variables

Variable	Default	Description
`RAG_VECTOR_STORE`	`qdrant`	`qdrant`, `chroma`, `pgvector`
`RAG_EMBEDDER`	`voyage`	`voyage`, `tei` (falls back to `tei` if `RAG_VOYAGE_API_KEY` is not set)
`RAG_QDRANT_URL`	`http://localhost:6333`	Qdrant REST URL
`RAG_CHROMA_URL`	`http://localhost:8000`	Chroma REST URL
`RAG_PGVECTOR_DSN`	-	Postgres connection string
`RAG_TEI_URL`	`http://localhost:8081`	Text Embeddings Inference URL
`RAG_VOYAGE_API_KEY`	-	Voyage AI API key (required when `RAG_EMBEDDER=voyage`)
`RAG_VOYAGE_MODEL`	`voyage-4`	Voyage AI model name

Tools

rag_create_project — create a project collection
rag_delete_project — delete a project collection
rag_index — index individual documents into a project
rag_index_project — scan a directory and auto-index all code/text files. Handles insertions and deletions (removed files are purged from RAG). Skips node_modules, .git, vendor, dist, build.
rag_semantic_search — semantic search across project memory
rag_retrieve_context — compact context string for LLM consumption

Notes

The MCP server uses stdio transport by default.
Each project gets its own collection/index: project_<project_id>.
The existing Coolify robloxkit-rag stack exposes Qdrant on localhost:6333 and TEI on localhost:8081 when Docker/Colima is running.

Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
mcp-server		mcp-server
skill		skill
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
README.md		README.md

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enowx-rag

Quick setup (copy-paste this to your AI agent)

What agents should do

1. Install the Factory Droid skill

2. Build the MCP server

3. Start the RAG backend (local mode)

4. Install the MCP server in every coding tool

Quick reference table

Claude Code

Claude Desktop

Cline

Cursor

OpenCode

Codex (OpenAI)

Factory Droid

Roo Code

Zed

Windsurf

Continue

5. Enable RAG memory for the target project

Per-project collection

AGENTS.md and CLAUDE.md

AGENTS.md

CLAUDE.md

Layout

Providers

Embedding options

Option A: Voyage AI (recommended, hosted)

Option B: TEI (self-hosted)

Environment variables

Tools

Notes

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`AGENTS.md`

`CLAUDE.md`

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