CAP - Codebase Awareness Protocol

Give AI coding agents the context they need - architecture, dependencies, and API - so they stop guessing and start respecting your codebase.

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Philosophy

AI coding agents are powerful, but they are blind. They see files, not architecture. They read imports, not intent. Without explicit context, they silently violate boundaries, import forbidden packages, and ignore the structure you spent months building.

CAP fixes this. You describe your codebase once in three small YAML files. CAP serves that knowledge to any AI agent through the Model Context Protocol (MCP), the open standard for connecting AI to tools.

The result: agents that understand your layers, respect your dependency rules, and use your public API - without you repeating yourself in every prompt.

Three principles

1. Declarative over discovery - You know your codebase better than static analysis. Write it down once, in plain YAML.
2. Protocol over prompts - Structured MCP tool responses beat pasted context every time.
3. Zero lock-in - YAML files live in your repo. No SaaS, no accounts, no telemetry.

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Getting Started

VS Code Extension (recommended)

The fastest path. The extension handles Python, installs CAP, runs the MCP server, and connects it to your AI agent - all automatically.

1. Install the extension

Search for "CAP - Codebase Awareness Protocol" in the VS Code Extensions Marketplace, or install from the command line:

code --install-extension domasles.cap-vscode

2. Initialize your workspace

Open the Command Palette (Ctrl+Shift+P) and run:

CAP: Initialize Configuration

This creates a .cap/ directory with three template files:

.cap/
├── api.yaml            # What your code exports
├── architecture.yaml   # How your code is organized
└── dependencies.yaml   # What your code depends on

Edit them to match your project. That's all the setup you need.

3. Enable the MCP server

When .cap/ is created, the extension shows a notification prompting you to enable the MCP server. Click Open Chat to go to the Copilot panel, then enable the CAP server in the MCP tools list.

Once enabled, every AI agent conversation has access to three tools:

Tool	What the agent gets
get_architecture	Layers, modules, ownership, architectural rules
get_dependencies	Runtime/dev packages, versions, forbidden patterns
get_api	Public/internal exports, stability, access rules

The agent calls these tools when it needs context. You don't need to prompt for it.

CLI

If you prefer working from the terminal or want CAP without VS Code:

pip install cap-cli

Initialize:

cap init              # Create .cap/ with templates
cap init --minimal    # Bare minimum scaffolding
cap init --force      # Overwrite existing files

Validate your configuration:

cap validate          # Pretty terminal output
cap validate --json   # Machine-readable for CI

Start the MCP server:

cap serve

This runs a stdio MCP server that any MCP-compatible client can connect to. Point your AI tool at the cap serve command and it will discover the three tools automatically.

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The .cap/ Files

architecture.yaml

Defines the structural skeleton of your codebase.

architecture:
  style: "hexagonal"

layers:
  domain:
    owns: "src/domain/**"
    may_import: []

  application:
    owns: "src/application/**"
    may_import:
      - "domain"

  infrastructure:
    owns: "src/infrastructure/**"
    may_import:
      - "domain"

modules:
  core:
    owns: "src/core/**"
    purpose: "Core business logic and domain models"

rules:
  forbid:
    - path: "src/domain/**"
      calls: "requests.get"
      reason: "Domain must not make HTTP calls"

dependencies.yaml

Lists every dependency with its purpose and defines what layers cannot use.

dependencies:
  python:
    runtime:
      pydantic:
        version: ">=2.0"
        reason: "Data validation using type annotations"
    dev:
      pytest:
        version: ">=7.0"
        reason: "Testing framework"

rules:
  forbid:
    - layer: "domain"
      dependency: "requests"
      reason: "Domain should not make HTTP calls directly"

notes:
  - "Prefer standard library when possible"

api.yaml

Maps what your code exports and who should use it.

api:
  public:
    core:
      location: "src/api.py"
      exports:
        - "initialize"
        - "process_data"
      stability: "stable"

  internal:
    utilities:
      location: "src/utils/**"
      exports:
        - "helper_function"
      stability: "experimental"
      warning: "Internal only - use the public API instead"

rules:
  forbid:
    - path: "src/plugins/**"
      api: "internal.utilities"
      reason: "Plugins must use the public API"

All fields are validated with strict schemas. Unknown keys are rejected. Run cap validate to catch mistakes before your agent does.

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Architecture

CAP itself follows hexagonal architecture. The monorepo contains three packages with a strict one-way dependency flow:

Dependency flow: cli -> mcp -> core. Never the reverse.

cap/
├── core/                   # cap-core - parsing, validation, formatting
│   └── cap_core/
│       ├── domain/         # Pydantic models - pure data, no I/O
│       │   └── models/
│       ├── infrastructure/ # FileReader - the only class that touches disk
│       └── application/    # ConfigService, ValidationService, MCPFormatter
│           └── services/
├── mcp/                    # cap-mcp - FastMCP server with stdio transport
│   └── cap_mcp/
│       ├── server.py       # Composition root - wires ConfigService into tools
│       └── tools/          # One file per MCP tool
├── shells/
│   ├── cli/                # cap-cli - click-based CLI
│   │   └── cap_cli/
│   │       ├── commands/   # init, serve, validate
│   │       ├── templates/  # Default .cap/ YAML files
│   │       └── utils/      # Workspace detection, output helpers
│   └── vscode/             # VS Code extension - TypeScript
│       └── src/
│           ├── setup/      # Python discovery, venv management, updates
│           ├── cap/        # CLI runner, init commands, validation
│           ├── mcp/        # MCP server provider, enable notice
│           └── utils/      # Shared workspace/venv/watcher helpers
└── .cap/                   # CAP's own configuration (we eat our own dogfood)

Key design decisions

• Domain models are pure data. Pydantic BaseModel subclasses with extra="forbid" - no I/O, no side effects, strict validation.
• ConfigService is the main facade. All config loading goes through it. Shells never touch FileReader directly.
• One MCP tool per file. Each tool module exposes a register(mcp, config_service) function.
• The VS Code extension calls cap as a subprocess. It never imports Python - it installs cap-cli into an isolated venv and runs commands through it.

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Development

Prerequisites

• Python ≥ 3.11
• Node.js ≥ 20 (VS Code extension only)
• VS Code ≥ 1.109.0 (VS Code extension only)
• Git (for version control integration)

Setup

Clone and install all packages in editable mode:

git clone https://github.com/domasles/cap.git
cd cap

pip install -e core/
pip install -e mcp/
pip install -e shells/cli/

Verify everything works:

cap --version
cap validate

VS Code Extension Development

cd shells/vscode
npm install
npm run build

Press F5 in VS Code to launch the Extension Development Host. In dev mode, the extension skips venv setup and expects cap to be installed on your PATH (from the editable installs above).

Project structure cheat sheet

Package	Language	Entry point	Install
cap-core	Python	cap_core.ConfigService	pip install -e core/
cap-mcp	Python	cap_mcp.create_server()	pip install -e mcp/
cap-cli	Python	cap CLI command	pip install -e shells/cli/
cap-vscode	TypeScript	src/extension.ts	npm install in shells/vscode/

Running tests

cap validate --json    # Validate .cap/ files

Adding a new shell

CAP is designed to be extended with new shells (IDE plugins, web UIs, etc.). A shell:

1. Calls cap CLI commands as a subprocess - it does not import core or mcp directly
2. Never adds logic to core - it only wires CLI output into its own UI
3. Lives in shells/<name>/
4. Gets its own build system and entry point

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Contributing

Contributions are welcome. Before you start:

1. Open an issue first for non-trivial changes so we can discuss the approach.
2. Run cap validate to make sure your .cap/ files are correct.
3. Respect the architecture. The layer rules in architecture.yaml aren't suggestions - they're the contract.
4. Keep the dependency flow. cli -> mcp -> core. If you find yourself importing from a shell into core, stop and rethink.

Style

• Python: Black formatter, type hints everywhere
• TypeScript: Strict mode, no any
• YAML: 2-space indent, comments for anything non-obvious

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License

Apache License 2.0