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(Still building...) Context store for AI agents. Draft specs, PRDs, plans, and conventions with AI assistance, store them once, and let agents retrieve relevant context automatically via MCP.

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Contextor Monorepo

Contextor is a context store for AI agents. It helps teams draft specs, PRDs, plans, and conventions with AI assistance, store them once, and let agents retrieve relevant context automatically via MCP—no more manual copy/paste of project knowledge.

This repository is a Bun-powered monorepo. The primary application lives in packages/app, and supporting assets (documentation, MCP tooling experiments, etc.) live alongside it.

Getting Started

# Install workspace dependencies
bun install

Useful Commands

  • bun run dev – start the Contextor app locally (Vite dev server)
  • bun run build – build all workspaces (use --filter <workspace> for targeted builds)
  • bun run check-types – run TypeScript project references in no-emit mode
  • bun run lint – run Biome with autofix enabled
  • bun run test – execute Vitest against the app workspace (TypeScript targets only)
  • bun run release – build the MCP server workspace and publish with Changesets (CI usually handles this)

Vitest currently focuses on non-React TypeScript modules (Convex helpers, utilities, etc.). Extend coverage as you add critical logic.

Workspace Layout

  • packages/app – Frontend/Convex implementation of the context store
  • docs – Product plans, specifications, and project overview docs
  • packages/mcp-server – MCP integration tooling (experiments)

Each workspace manages its own scripts; use bun run --filter <workspace> <script> for workspace-specific tasks.

Contributing

  1. Create a feature branch.
  2. Make changes scoped to a single concern.
  3. Run bun run check-types, bun run lint, and (when applicable) bun run test.
  4. Submit a PR with screenshots of UI changes and notes about any backend/schema updates.

Refer to AGENTS.md for detailed conventions around project structure, naming, and agent responsibilities.

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(Still building...) Context store for AI agents. Draft specs, PRDs, plans, and conventions with AI assistance, store them once, and let agents retrieve relevant context automatically via MCP.

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