[Case Study]
One brief,
every agent
Context Brain is the context layer that briefs every AI coding agent over MCP - live-indexed, scoped to the task, and shared across every agent working the same codebase.
Sector
AI Context Layer & DevTools
Engagement
Full Product Build
Users
Engineering Teams & AI Coding Agents
[Overview]
A context layer
for AI agents
The brief was simple to state and hard to build: every AI coding agent touching a codebase should start from the same, current understanding of it - not a stale README, and not whatever fit in that session's context window.
We built Context Brain as an MCP server that sits between a team's codebase and every agent that touches it - Claude Code, Cursor, Windsurf, or anything else speaking MCP - continuously indexing code, docs, and history into scoped, task-specific briefs.
Agents don't just receive context, they contribute to it. Decisions and findings from one session persist so the next agent - or the next engineer - picks up exactly where things left off.
[The Challenge]
Three problemsnobody was solving together
Context scattered everywhere
Code, docs, tickets, and design decisions lived across a dozen tools, so every AI coding agent started each task from zero context.
No shared memory between agents
Claude Code, Cursor, and other agents working on the same repo had no way to share what they'd already learned, tried, or decided.
Context that goes stale fast
Static prompt files and README dumps couldn't keep pace with a fast-moving codebase, so agents kept working off outdated assumptions.
[What We Built]
Every layer designed aroundshared context
Universal MCP context server
A single MCP endpoint that briefs any connected AI coding agent with exactly the context it needs for the task at hand.
Live repository indexing
Continuously indexes code, docs, and commit history so agents work from current state instead of a stale snapshot.
Cross-agent memory
Decisions, conventions, and findings from one agent's session persist, so the next agent picks up mid-thread instead of starting over.
Scoped context briefs
Per-task context packages — only the files, docs, and history relevant to what the agent is actually working on, nothing more.
Agent activity dashboard
Real-time visibility into which agents are active, what they're querying, and how much context they're pulling from each repo.
Access & permission controls
Fine-grained control over which repos, docs, and secrets each agent or team is allowed to see, enforced at the context layer.
[Outcome]
What shipped
Universal
MCP-based context for any AI agent
Real-time
Live-indexed repo & doc context
Cross-agent
Shared memory across sessions
Scoped
Per-task context, not everything
[Tech Stack]