Portfolio memory for AI agents.
Cortex MCP Server
Portfolio memory for AI agents.
Transforms your real project history into structured context that any AI assistant can query in real time.
⚠️ Status: Active MVP — functional and tested, but under active development. Feedback and contributions are welcome.
What It Is
Cortex MCP is a local server that implements the Model Context Protocol (MCP) — the open standard that lets AI assistants safely access external data.
It reads synthesized knowledge about your projects (a lightweight adjacency knowledge graph mapping relations between apps, technologies, and domains; patterns; observations; developer profile) and exposes it as tools, resources, and prompts consumable by any MCP-compatible agent.
The Problem
When you open Claude, Copilot, or Cursor in a project, the agent does not know:
- What other projects you have and how they connect
- Which stack you prefer and why
- Which patterns you use repeatedly
- Which components could be reused
- Your real experience with each technology
Every session starts from scratch — amnesiac pair programming.
The Solution
Cortex gives AI agents the same long-term memory you have as a developer. It accumulates knowledge across repositories and makes it instantly accessible in every session.
"AI is your mirror — it reveals who you are faster. If you are incompetent, it will produce bad things faster. If you are competent, it will produce good things faster." — Akita
Cortex works like the "CLAUDE.md" of your entire portfolio — not of one project, but of your whole career.
| Problem | Without Cortex | With Cortex |
|---|---|---|
| Agent suggests a stack | Generic, based on popularity | Based on your real history |
| Agent solves a problem | Standard solution, may reinvent the wheel | "You already did this in X, here it is" |
| Architecture decision | Over-engineering (agent never says no) | "Your pattern is to simplify, see Y" |
| Context lost between sessions | Starts from zero every time | Accumulates decisions, patterns, pitfalls |
Complete Privacy
100% local, no cloud, no telemetry. Your data never leaves your machine.
How It Works — The Three Layers
Read this section before using. Most users only use Layer 1 and find the system "shallow". The real value is in Layers 2 and 3.
Cortex does not learn passively. It is a structured knowledge repository — the more you feed it, the more useful it becomes. The right mental model: a portfolio wiki that AI agents query in real time.
Layer 1 — Automatic Scanner (~20% of value)
cortex-mcp scan automatically detects:
- Technology stack (language, frameworks, databases, CI, Docker)
- Commit frequency and contributor history
- Recurring patterns across repositories
- Initial operator profile
Important limitation: the scanner only sees what is in the code and git history. It does not know why you chose a technology, what problems you encountered, or what you learned. A project with 1 commit (shallow clone) generates a very poor profile.
Layer 2 — Curation via MCP Tools (~60% of value)
The real power of Cortex is fed by you during work sessions:
| Tool | When to use | Example |
|---|---|---|
add_observation |
When you learn something relevant, find a pi |
Tools (1)
add_observationRecords a specific observation or lesson learned during a work session to the knowledge graph.Configuration
{"mcpServers": {"cortex": {"command": "npx", "args": ["-y", "@bugg1n/cortex-mcp"]}}}