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kawa.mcp

Reference implementation of the Kawa Code MCP protocol.

Overview

The Kawa Code MCP server provides the communication layer used by Kawa Code to record and align development intent between developers and AI systems.

It enables:

• Persistent AI reasoning context • Intent tracking during development workflows • Alignment between human and AI decisions over time

This repository contains the reference implementation of the Kawa MCP server used by Kawa Code tools.

This MCP server enables AI coding assistants to understand what you're working on and maintain context across sessions. It connects to the Kawa Code desktop application to provide:

  • Intent tracking: Create and manage development intents with decision history
  • Team collaboration: See what teammates are working on, detect conflicts
  • Decision recording: Track architectural decisions and trade-offs with constraint validation
  • Code block assignment: Associate code changes with intents for better commit history

Usage

The MCP server works together with Kawa Code, AI code generators such as Cursor, Claude Code, and the Kawa Code extensions.

Key Features

  • Context Persistence: Never lose track of what you were working on across AI sessions
  • Smart Context Retrieval: Relevance-based context loading - only fetch what's needed for the current task
  • Zero-Knowledge Encryption: Code blocks encrypted client-side before cloud sync, API cannot decrypt
  • Team Conflict Detection: Know when teammates are working on the same files/lines
  • Decision Tracking: Record architectural decisions with constraint validation and conflict detection
  • Commit Integration: Link all code changes to intent context for better git history
  • Cross-Platform: Works with Claude Code and Cursor AI via MCP protocol

Prerequisites

Required

  • Node.js >= 18.0.0 — runtime for the MCP server
  • Kawa Code desktop app running — kawa.mcp is a thin MCP-to-IPC adapter; all git operations, storage, and API communication happen in Kawa Code
  • Active Kawa Code account — for cloud sync and team features

Optional (for history inference)

  • Anthropic API key — your own Claude API key, passed as a parameter to the inference tools
  • GitHub CLI (gh) — enables richer data tiers (PR descriptions, review comments, issue discussions). Without gh, tiers 2 and 4 are skipped automatically

Installation

# Clone the repository (if not already cloned)
cd /path/to/kawa.mcp

# Install dependencies
npm install

# Build the TypeScript source
npm run build

Quick Start

  1. Start Kawa Code: Launch the Kawa Code desktop app and log in
  2. Configure MCP: Add kawa.mcp to your AI assistant's MCP configuration (see Configuration section)
  3. Restart AI: Restart Claude Code or Cursor to load the MCP server
  4. Test connection: The server will try to connect to Kawa Code on startup
  5. Start coding: Use check_active_intent to begin tracking your work

Setting Up CLAUDE.md

For Claude Code to use the MCP tools effectively, your project needs a CLAUDE.md file that tells Claude when to call the tools and provides your repository coordinates.

Copy the example template into your project root and fill in the placeholders:

cp /path/to/kawa.mcp/CLAUDE.md.example /path/to/your-project/CLAUDE.md

Or just merge the example with your own content. See CLAUDE.md.example for the full template with optional sections for monorepos, code style, and architecture.

Configuration

Claude Code

Create a .mcp.json file in your project root (recommended for teams — commit it to git):

{
  "mcpServers": {
    "kawa-intents": {
      "type": "stdio",
      "command": "node",
      "args": ["/absolute/path/to/kawa.mcp/build/index.js"]
    }
  }
}

Or add it at user level (available across all your projects):

claude mcp add --transport stdio kawa-intents --scope user -- node /absolute/path/to/kawa.mcp/build/index.js

Cursor AI

Add to your Cursor MCP configuration (~/.cursor/mcp.json):

{
  "mcpServers": {
    "kawa-intents": {
      "command": "node",
      "args": ["/absolute/path/to/kawa.mcp/build/index.js"]
    }
  }
}

Important: Use absolute paths, not relative paths or ~ shortcuts.

Available Tools

Context & Discovery

Tool Description
get_relevant_context Get context relevant to a specific task

Intent Management

Tool Description
check_active_intent Check if there's an active intent before starting new work
create_and_activate_intent Create and activate a new intent for a development task
get_intents_for_file Get intents (team and self) affecting a specific file
get_intents_for_lines Get intents affecting specific line ranges in a file
assign_blocks_to_intent Assign modified line ranges to the active intent
get_intent_changes Get uncommitted changes for the active intent
complete_intent Complete an intent (committed/done/abandoned)
list_team_intents List what teammates are currently working on

Decision Recording

Tool Description
record_decision Record an architectural decision with rationale and constraint validation
get_session_decisions Get decisions recorded during the current session
get_project_decisions Get all decisions across all intents for the project
edit_session_decision Edit or delete a decision before intent completion
detect_intent_conflicts Detect if current intent decisions conflict with team decisions

History Inference

Tool Description
infer_history Analyze git commit history to extract development stories and decisions
evolve_decisions Build a decision evolution graph from previously extracted stories

Lightweight Logging

Tool Description
log_work Log completed work without the full intent lifecycle — use for quick fixes and trivial changes

MCP Capabilities

Prompts

The server exposes prompts that can be loaded into your AI coding session:

  • implementation_workflow: Standard workflow for implementing code changes with intent tracking. Provides step-by-step guidance on checking for active intents, creating new intents, checking for conflicts, and assigning blocks.

Resources

The server exposes resources that can be monitored:

  • kawa://intent/active: Real-time view of the currently active intent for the connected repository (JSON format)

History Inference

Two MCP tools analyze git commit history to extract structured development knowledge — useful for bootstrapping a repository with historical context.

infer_history

Runs the full pipeline automatically: infer → evolve → persist.

  1. Pass 1: Groups commits into coherent development stories with value hints (high/low/none)
  2. Pass 2: Deep analysis of high/low-value stories to extract architectural decisions and lessons learned
  3. Evolution: Curates decisions by finding relationships (supersedes, reinforces, contradicts, specializes)
  4. Persist: Stores curated stories as intents with decisions and lessons (auto-syncs to cloud)

The pipeline runs asynchronously inside Kawa Code. Progress is shown in the Kawa Code desktop app via a progress bar. The pipeline supports checkpointing — if interrupted, re-running resumes from where it left off.

Usage in Claude Code:

Use the infer_history tool with estimateOnly: true to preview the cost first,
then run it with estimateOnly: false.

Parameters:

Parameter Type Default Description
repoPath string (required) Local path to the repository root
apiKey string (required) Your Anthropic API key
commits number 50 Number of recent commits to analyze
tier number 4 Data enrichment tier (1-5, see below)
model string claude-sonnet-4-20250514 Anthropic model to use
maxStories number 0 Limit stories to analyze in Pass 2 (0 = unlimited)
allowCommitSplitting boolean false Allow splitting a commit into multiple stories when it contains unrelated changes (recommended for repos with messy commit history)
contextIssues boolean false Include context issues from commit date range (tier 4 only)
estimateOnly boolean false Preview token cost without running the pipeline

evolve_decisions

Builds a decision evolution graph from previously extracted stories — identifying how decisions relate across stories over time. Note: infer_history already chains evolve + persist automatically. Use this tool only if you want to run evolution separately on a pre-existing set of stories.

  1. Bucketing: Groups stories by file overlap and keyword similarity
  2. Edge classification: Uses LLM to identify relationships (supersedes, reinforces, contradicts, specializes)
  3. Annotation: Labels each decision as stable, orphan, evolved, or abandoned
  4. Curation: Keeps stable + orphan decisions, drops evolved + abandoned

If repoPath is provided, curated stories are automatically persisted as intents with decisions and lessons after evolution completes.

Parameters:

Parameter Type Default Description
stories array (required) Story objects from a previous infer_history run
apiKey string (required) Your Anthropic API key
model string claude-haiku-4-5-20251001 Anthropic model (cheaper model recommended)
repoPath string (optional) Local path to repository root (required for auto-persist)
repoOrigin string (optional) Git remote origin URL (auto-detected from repoPath if not provided)

Data Tiers

Each tier adds more context for better inference. Higher tiers require the gh CLI authenticated.

Tier Data source Requires gh
1 Commit messages + numstat No
2 + PR descriptions and review comments Yes
3 + Diffs for revert commits No
4 + Referenced GitHub issues (default) Yes
5 + Diffs for all commits with annotation extraction No

Development

# Watch mode (auto-rebuild on file changes)
npm run dev

# Build TypeScript to JavaScript
npm run build

# Clean build artifacts
npm run clean

# Run the MCP server directly
npm start

Testing the MCP Server

To test the MCP server without integrating it into an AI assistant:

  1. Build the project: npm run build
  2. Run the server: npm start
  3. The server communicates via stdio (standard input/output)
  4. You can send MCP protocol messages via stdin to test tool functionality

Development Tips

  • Use npm run dev to auto-rebuild during development
  • Check stderr for server logs (stdout is reserved for MCP protocol)
  • Ensure Kawa Code is running before testing

Architecture

Claude Code / Cursor AI
    ↓ MCP Protocol (stdio)
kawa.mcp (this server)
    ↓ Huginn IPC (Unix socket / Named pipe)
Kawa Code Desktop App
    ├─ Gardener Module (Rust)
    │   └─ Intent/Decision storage
    └─ HTTP Client
        ↓ REST + SSE
    Kawa API (cloud)
        └─ Team sync & encryption

The MCP server communicates with Kawa Code using the Huginn IPC protocol:

  • Context queries: Intents, decisions, relevant context
  • Intent operations: Create, update, assign blocks, complete
  • Decision tracking: Record, retrieve, edit, conflict detection

Kawa Code's Gardener module handles all git operations, diff generation, and local storage of encrypted data.

Contributing

Contributions are welcome. Please read CONTRIBUTING.md and CLA.md.

License

This project is source-available under the Kawa Code Source Available License.

You may run and modify the software for personal or internal use.

See LICENSE for details.

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