How do I use Context Intelligence Layer?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Context Intelligence Layer Store that I prefer Python for backend projects." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Context Intelligence Layer

by myselfvivek17

Overview Schema Related Servers Score Discussions

Python

Remote

Context Intelligence Layer

A model-agnostic middleware that gives LLMs persistent memory and reusable skills via the Model Context Protocol (MCP).

Why this exists

Every time you start a new conversation with an LLM, it forgets everything — your preferences, past decisions, project context, and workflows you've already explained. You end up repeating yourself across sessions.

This project solves that. It gives any MCP-compatible model a long-term memory and a skill library backed by a vector database. Memories are stored semantically, so the model retrieves them by meaning — not exact keywords. Skills let you save multi-step procedures once and have the model follow them automatically in future sessions.

The key design choice: model-agnostic. This isn't locked to Claude or GPT. Any client that speaks MCP (Claude Desktop, Claude Code, Codex, LiteLLM, or anything built tomorrow) can plug in and instantly get persistent context. Switch models, keep your memory.

What it can do

Remember who you are, what you're working on, and how you like things done
Recall decisions from weeks-old conversations without you repeating them
Store deployment checklists, debugging workflows, or review processes as reusable skills
Work across multiple AI clients simultaneously — same memory, different models

Related MCP server: engram

Features

Persistent memory — store facts, preferences, decisions, and goals across conversations
Semantic search — retrieve memories by meaning, not just keywords
Reusable skills — save step-by-step instructions that any LLM can find and follow
Domain-scoped storage — memories organized into identity, projects, code, general
Bearer token auth — API key protection on every tool call
Model-agnostic — works with any MCP client (Claude Desktop, Claude Code, Codex, etc.)

Architecture

MCP Client (Claude / Codex / etc.)
        │
        │  MCP (Streamable HTTP)
        ▼
 context-mcp server  ←─── FastMCP 3.x + Python
        │
        │  Qdrant client
        ▼
   Qdrant (vector DB)

Project Structure

context-intelligence/
│
├── server/                        # ── Core Server ──
│   ├── main.py                    # MCP server entry point, tool definitions, auth setup
│   ├── qdrant_store.py            # Qdrant CRUD — store, search, delete operations
│   ├── schemas.py                 # Pydantic models (MemoryEntry, SkillEntry)
│   └── embeddings.py              # FastEmbed wrapper (all-MiniLM-L6-v2, 384 dims)
│
├── setup/                         # ── Setup & Config ──
│   └── init_collections.py        # One-time script to create Qdrant collections
│
├── docs/                          # ── Documentation ──
│   └── SYSTEM_PROMPT.md           # Drop-in system prompt for LLM clients
│
├── Dockerfile                     # Container build for the MCP server
├── requirements.txt               # Python dependencies
├── README.md                      # You are here
├── LICENSE                        # MIT
└── .gitignore

MCP Tools

Tool	Description
`store_memory_tool`	Store a memory in a domain collection
`search_memory_tool`	Semantic search across memories
`delete_memory_tool`	Delete a memory by ID
`store_skill_tool`	Save a reusable skill with instructions
`find_skill_tool`	Find relevant skills by intent
`list_skills_tool`	List all stored skills

Quick Start

Prerequisites

Docker + Docker Compose

1. Clone and build the image

git clone https://github.com/myselfvivek17/context-intelligence.git
cd context-intelligence
docker build -t context-mcp:latest .

2. Create `docker-compose.yml`

services:
  qdrant:
    image: qdrant/qdrant
    network_mode: host
    volumes:
      - /data/qdrant:/qdrant/storage
    environment:
      - QDRANT__SERVICE__API_KEY=your-qdrant-key
    restart: unless-stopped

  context-mcp:
    image: context-mcp:latest
    network_mode: host
    environment:
      - QDRANT_URL=http://localhost:6333
      - QDRANT_API_KEY=your-qdrant-key
      - FASTMCP_HOST=0.0.0.0
      - FASTMCP_PORT=8083
      - MCP_API_KEY=your-mcp-api-key
      - MAX_SEARCH_LIMIT=50
    restart: unless-stopped

Note: Replace your-qdrant-key and your-mcp-api-key with your own random strings — these are secrets you create, not values you get from anywhere. Use a password generator or something like openssl rand -base64 24.

3. Start the stack

docker compose up -d

4. Initialize Qdrant collections (run once)

Wait a few seconds for Qdrant to start, then:

With Docker:

docker run --rm --network host \
  -e QDRANT_URL=http://localhost:6333 \
  -e QDRANT_API_KEY=your-qdrant-key \
  context-mcp:latest \
  python setup/init_collections.py

With Python (if installed locally):

pip install qdrant-client
QDRANT_URL=http://your-server:6333 QDRANT_API_KEY=your-qdrant-key python init_collections.py

This creates the 5 required Qdrant collections:

Collection	Purpose
`memory_identity`	User preferences, personal facts, who the user is
`memory_projects`	Ongoing work, goals, decisions, project context
`memory_code`	Languages, frameworks, coding patterns, conventions
`memory_general`	Everything else that doesn't fit above
`skills`	Reusable step-by-step instructions for the LLM to follow

Configuration

Environment Variable	Default	Description
`QDRANT_URL`	`http://localhost:6333`	Qdrant server URL
`QDRANT_API_KEY`	(none)	Qdrant API key
`MCP_API_KEY`	(none)	Bearer token for MCP auth
`FASTMCP_HOST`	`0.0.0.0`	Server bind host
`FASTMCP_PORT`	`8083`	Server port
`MAX_SEARCH_LIMIT`	`50`	Max results per search query

Connecting MCP Clients

Claude Code (`.mcp.json`)

{
  "mcpServers": {
    "context-intelligence": {
      "command": "npx",
      "args": [
        "--yes", "mcp-remote",
        "http://your-server:8083/mcp",
        "--allow-http",
        "--header", "Authorization: Bearer your-mcp-api-key"
      ]
    }
  }
}

Claude Desktop — Windows (`claude_desktop_config.json`)

{
  "mcpServers": {
    "context-intelligence": {
      "command": "cmd",
      "args": [
        "/c", "npx", "--yes", "mcp-remote",
        "http://your-server:8083/mcp",
        "--allow-http",
        "--header", "Authorization: Bearer your-mcp-api-key"
      ]
    }
  }
}

Codex (`~/.codex/config.toml`)

[[mcp_servers]]
name = "context-intelligence"
command = "npx"
args = ["--yes", "mcp-remote", "http://your-server:8083/mcp", "--allow-http", "--header", "Authorization: Bearer your-mcp-api-key"]

System Prompt

To enable automatic memory behavior in your AI client, see SYSTEM_PROMPT.md. It instructs the model to proactively search and store memories without being asked.

Memory Domains

Domain	Use for
`identity`	User preferences, personal facts
`projects`	Ongoing work, goals, decisions
`code`	Languages, patterns, tools, conventions
`general`	Everything else

Tech Stack

FastMCP — MCP server framework
Qdrant — Vector database
FastEmbed — Local embeddings (all-MiniLM-L6-v2, 384 dims)
mcp-remote — stdio-to-HTTP bridge for MCP clients

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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Context Intelligence Layer

Why this exists

What it can do

Features

Architecture

Project Structure

MCP Tools

Quick Start

Prerequisites

1. Clone and build the image

2. Create docker-compose.yml

3. Start the stack

4. Initialize Qdrant collections (run once)

Configuration

Connecting MCP Clients

Claude Code (.mcp.json)

Claude Desktop — Windows (claude_desktop_config.json)

Codex (~/.codex/config.toml)

System Prompt

Memory Domains

Tech Stack

License

Maintenance

Resources

Looking for Admin?

Latest Blog Posts

MCP directory API

2. Create `docker-compose.yml`

Claude Code (`.mcp.json`)

Claude Desktop — Windows (`claude_desktop_config.json`)

Codex (`~/.codex/config.toml`)