Sekha MCP Server

Model Context Protocol Server for Sekha Memory

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🆕 v0.2.0 Release - Multi-Provider Support

Sekha MCP v0.2.0 is now compatible with the new Sekha v0.2.0 multi-provider architecture!

What's New:

• ✅ Works with Sekha v0.2.0 controller's multi-provider routing
• ✅ Automatic provider fallback (Ollama, OpenAI, Anthropic, etc.)
• ✅ Vision support (GPT-4o, Kimi 2.5) - just include images!
• ✅ Cost-aware model selection
• ✅ Multi-dimensional embeddings (per-dimension ChromaDB collections)
• ✅ Claude Desktop & Claude Code support - memory in both apps!
• ✅ No API changes - fully backward compatible!

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What is Sekha MCP?

MCP (Model Context Protocol) server that exposes Sekha memory tools to any MCP-compatible client:

• ✅ Claude Desktop - Anthropic's desktop app
• ✅ Claude Code - VS Code extension (works with Ollama, Anthropic, or any provider)
• ✅ Any MCP client - Standard protocol implementation

Supported Tools:

• ✅ memory_store - Save conversations
• ✅ memory_search - Semantic search
• ✅ memory_get_context - Retrieve relevant context
• ✅ memory_update - Update conversation metadata
• ✅ memory_prune - Get cleanup recommendations
• ✅ memory_export - Export your data
• ✅ memory_stats - View usage statistics

Total: 7 MCP tools

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📚 Documentation

Complete guide: docs.sekha.dev/integrations/mcp

• Claude Desktop Integration
• Claude Code Integration
• MCP Tools Reference
• Getting Started

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🚀 Quick Start

1. Install Sekha

# Deploy Sekha v0.2.0 stack with multi-provider support
git clone https://github.com/sekha-ai/sekha-docker.git
cd sekha-docker
docker compose -f docker/docker-compose.prod.yml up -d

2. Configure Your MCP Client

Option A: Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "sekha": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--network=host",
        "ghcr.io/sekha-ai/sekha-mcp:v0.2.0"
      ],
      "env": {
        "CONTROLLER_URL": "http://localhost:8080",
        "CONTROLLER_API_KEY": "your-mcp-api-key-here"
      }
    }
  }
}

Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

Option B: Claude Code (VS Code Extension)

Add to VS Code settings.json or workspace config:

{
  "mcpServers": {
    "sekha": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--network=host",
        "ghcr.io/sekha-ai/sekha-mcp:v0.2.0"
      ],
      "env": {
        "CONTROLLER_URL": "http://localhost:8080",
        "CONTROLLER_API_KEY": "your-mcp-api-key-here"
      }
    }
  }
}

Claude Code Configuration:

Claude Code lets you choose your LLM provider separately from memory:

{
  // Sekha provides memory (via MCP)
  "mcpServers": {
    "sekha": { /* config above */ }
  },
  
  // Configure your LLM provider (Claude Code supports multiple)
  "claudeCode.apiProvider": "ollama",  // or "anthropic"
  "claudeCode.ollamaUrl": "http://localhost:11434",
  "claudeCode.ollamaModel": "llama3.1:8b"
}

This means:

• Use Ollama (or other LLM) locally for generation (fast, private, free)
• Use Sekha MCP for memory (persistent across sessions)
• Best of both worlds!

3. Restart Your Client

• Claude Desktop: Restart the app
• Claude Code: Reload VS Code window (Cmd+Shift+P → "Reload Window")

Sekha memory tools will now appear!

See setup guides:

• Claude Desktop setup
• Claude Code setup

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🎯 Use Cases

Claude Desktop - Interactive Conversations

• Full-featured desktop app with Sekha memory
• Perfect for brainstorming, research, general chat
• Uses Anthropic's Claude models

Claude Code - Development Workflow Examples

• VS Code extension with code-aware features
• Use with Ollama for fast, local, private coding
• Or use with Anthropic/OpenAI for powerful cloud models
• Sekha memory works with any provider you configure

API Integration - Programmatic Access

• Use sekha-proxy for OpenAI-compatible API
• Multi-provider routing via LLM bridge
• Same memory as Claude apps

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🔧 Development

# Clone
git clone https://github.com/sekha-ai/sekha-mcp.git
cd sekha-mcp

# Install
pip install -e .

# Run locally
python -m sekha_mcp

# Test
pytest

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📚 MCP Tools Reference

memory_store

Store a conversation in Sekha.

Parameters:

• label (string) - Conversation label
• messages (array) - Message array (supports images in v0.2.0!)
• folder (string, optional) - Organization folder
• importance (int, optional) - 1-10 scale

memory_search

Search conversations semantically.

Parameters:

• query (string) - Search query
• limit (int) - Max results
• folder (string, optional) - Search within folder

memory_get_context

Assemble optimal context for LLM.

Parameters:

• query (string) - Context query
• context_budget (int) - Token limit
• folders (array, optional) - Limit to specific folders

memory_update

Update conversation metadata.

Parameters:

• conversation_id (string) - Conversation UUID
• label (string, optional) - New label
• folder (string, optional) - New folder
• importance (int, optional) - New importance (1-10)
• status (string, optional) - active/archived

memory_prune

Get cleanup recommendations.

Parameters:

• min_age_days (int, optional) - Minimum age
• max_importance (int, optional) - Max importance to consider
• limit (int, optional) - Max suggestions

memory_export

Export conversations.

Parameters:

• format (string) - json or markdown
• folder (string, optional) - Export specific folder

memory_stats

Get memory usage statistics.

Parameters: None

Returns:

• Total conversations
• Total messages
• Storage usage
• Folder breakdown
• Provider stats (v0.2.0) - which models are being used

Full API Reference

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🏗️ Architecture

┌─────────────────────────────────────────────┐
│  MCP Clients                                │
│  - Claude Desktop (Anthropic)               │
│  - Claude Code (Ollama/Anthropic/etc.)      │
│  - Any MCP-compatible client                │
└────────────────┬────────────────────────────┘
                 │
                 ▼
        ┌────────────────┐
        │  Sekha MCP     │ ← This repository
        │  Server        │
        └────────┬───────┘
                 │
                 ▼
        ┌────────────────┐
        │  Controller    │ ← Memory APIs
        │  (Rust)        │
        └────────┬───────┘
                 │
                 ▼
        ┌────────────────┐
        │  ChromaDB      │ ← Vector storage
        │  Redis         │ ← Cache
        └────────────────┘

Separate from LLM routing:
┌─────────────────────────────────────────────┐
│  API Clients → Proxy → Bridge → Providers   │
│  (OpenAI SDK compatible)                    │
└─────────────────────────────────────────────┘

Key Points:

• MCP provides memory tools only
• Claude Desktop/Code handle their own LLM connections
• Controller stores all conversations regardless of source
• Same memory accessible from Claude apps and API

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🔗 Links

• Main Repo: sekha-controller
• Proxy (API): sekha-proxy
• Docker Deploy: sekha-docker
• Docs: docs.sekha.dev
• Website: sekha.dev
• Discord: discord.gg/sekha

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📝 Changelog

See CHANGELOG.md for full release history.

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📝 License

AGPL-3.0 - License Details