CogZ MCP server

CogZ

Local-first, code-aware engineering cognition for AI coding agents.

CogZ gives a coding agent persistent memory, contextual retrieval, and continuous cognition about a software repository — all running locally on your machine, no cloud services required.

Works with Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, Devin, and any MCP-compatible agent.

What it looks like

Real output from CogZ running on its own codebase:

$ cogz context --mode task "token budget estimation and context pack compression"

Context pack (mode: task)
Query: token budget estimation and context pack compression
Search mode: hybrid
Sections: 91
Token estimate: 8188

Dropped: 6 sections over token budget

---

## 1. [rule] New expansion channels: emit early, filter before seen-mark, sort deterministically, never displace directs (relevance: 0.5148)

Conventions proven across the sibling and co-change channels:

1. Emit before the generic expansion loops — candidates emitted
   later get claimed-and-floored by graph traversal …
2. Apply entity-type/test filters BEFORE `seen.insert` …
…

## 2. [rule] cfg-gated code must be typechecked per-target before release (relevance: 0.4690)

Code behind #[cfg(unix)]/cfg(target_os = ...) is invisible to host
builds, tests, and clippy — a compile error in a cfg'd branch ships
silently until a real target build sees it. The v0.5.0 Windows leg
failure is the canonical example.

## 3. [rule] Degradation must be loud, never silent (relevance: 0.3680)

Every degraded or failed code path must surface a signal …

## 4. [identity] CogZ (relevance: —)

Project: CogZ

## 5. [file] assemble.rs (relev

That's not a text chunk from a vector search. The pack leads with validated rules — one learned from a release failure on this very project — plus the identity baseline and the actual source file, all ranked, traceable, and budgeted.

This repository already contains real dogfooding knowledge — CogZ has been used on its own codebase throughout development. You can clone it, install CogZ, and try the commands above against it directly.

What it does

CogZ maintains a project-specific knowledge layer that connects what an agent learns to the code it is working with.

Memory

CogZ stores three kinds of project knowledge:

• Observations — things an agent has learned or noticed. Raw, unvalidated experience: bugs found, decisions made, patterns noticed.
• Rules — validated knowledge that should influence future work. Coding standards, design decisions, confirmed patterns.
• Knowledge — structured information about the codebase. Architecture explanations, module responsibilities, trade-off rationale.

Shortened. The full README is on GitHub.