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MCP server for Claude session management and conversation memory

Project description

Session Management MCP Server

A dedicated MCP server that provides comprehensive session management functionality for Claude Code sessions across any project.

Features

  • 🚀 Session Initialization: Complete setup with UV dependency management, project analysis, and automation tools
  • 🔍 Quality Checkpoints: Mid-session quality monitoring with workflow analysis and optimization recommendations
  • 🏁 Session Cleanup: Comprehensive cleanup with learning capture and handoff file creation
  • 📊 Status Monitoring: Real-time session status and project context analysis

Available MCP Tools

Session Management

  • init - Comprehensive session initialization including:

    • Project context analysis and health monitoring
    • UV dependency synchronization
    • Session management setup with auto-checkpoints
    • Project maturity scoring and recommendations
    • Permissions management to reduce prompts
  • checkpoint - Mid-session quality assessment with:

    • Real-time quality scoring (project health, permissions, tools)
    • Workflow drift detection and optimization recommendations
    • Progress tracking and goal alignment
    • Automatic git checkpoint commits (if in git repo)
  • end - Complete session cleanup featuring:

    • Final quality checkpoint and assessment
    • Learning capture across key categories
    • Session handoff file creation for continuity
    • Workspace cleanup and optimization
  • status - Current session status including:

    • Project context analysis with health checks
    • Tool availability verification
    • Session management status
    • Available MCP tools listing with diagnostics

Memory & Reflection System

  • reflect_on_past - Search past conversations and insights with:

    • Semantic similarity search using local embeddings (all-MiniLM-L6-v2)
    • DuckDB-based conversation storage with FLOAT[384] vectors
    • Time-decay prioritization for recent conversations
    • Cross-project conversation history
    • Configurable similarity thresholds and result limits
  • store_reflection - Store important insights for future reference with:

    • Content indexing with semantic embeddings
    • Tagging system for organization
    • Project-specific context tracking
    • Automatic embedding generation (local, no external services)
  • search_nodes - Advanced search capabilities for stored knowledge

  • quick_search - Fast overview search with count and top results

  • get_more_results - Pagination support for large result sets

Permissions & Trust System

  • permissions - Manage trusted operations to reduce permission prompts:
    • View current trusted operations
    • Trust specific operations (UV sync, Git operations, file management)
    • Reset all permissions when needed

Installation

From Source

# Clone the repository
git clone https://github.com/lesleslie/session-mgmt-mcp.git
cd session-mgmt-mcp

# Install dependencies
uv sync --group dev

# Or use pip
pip install -e ".[embeddings,dev]"

MCP Configuration

Add to your project's .mcp.json file:

{
  "mcpServers": {
    "session-mgmt": {
      "command": "python",
      "args": ["-m", "session_mgmt_mcp.server"],
      "cwd": "/path/to/session-mgmt-mcp",
      "env": {
        "PYTHONPATH": "/path/to/session-mgmt-mcp"
      }
    }
  }
}

Alternative: Use Script Entry Point

If installed with pip/uv, you can use the script entry point:

{
  "mcpServers": {
    "session-mgmt": {
      "command": "session-mgmt-mcp",
      "args": [],
      "env": {}
    }
  }
}

Dependencies

Required:

  • Python 3.13+
  • fastmcp>=2.0.0 - MCP server framework
  • duckdb>=0.9.0 - Conversation storage database
  • numpy>=1.24.0 - Numerical operations for embeddings

Optional (for semantic search):

  • onnxruntime - Local ONNX model inference
  • transformers - Tokenizer for embedding models

Install with embedding support:

uv sync --extra embeddings
# or
pip install "session-mgmt-mcp[embeddings]"

Usage

Once configured, the following slash commands become available in Claude Code:

Primary Session Commands

  • /session-mgmt:init - Full session initialization with workspace verification
  • /session-mgmt:checkpoint - Quality monitoring checkpoint with scoring
  • /session-mgmt:end - Complete session cleanup with learning capture
  • /session-mgmt:status - Current status overview with health checks

Memory & Search Commands

  • /session-mgmt:reflect_on_past - Search past conversations with semantic similarity
  • /session-mgmt:store_reflection - Store important insights with tagging
  • /session-mgmt:quick_search - Fast search with overview results
  • /session-mgmt:permissions - Manage trusted operations

Advanced Usage

Running Server Directly (for development):

python -m session_mgmt_mcp.server
# or
session-mgmt-mcp

Testing Memory Features:

# The memory system automatically stores conversations and provides:
# - Semantic search across all past conversations
# - Local embedding generation (no external API needed)
# - Cross-project conversation history
# - Time-decay prioritization for recent content

Memory System Architecture

Built-in Conversation Memory

  • Local Storage: DuckDB database at ~/.claude/data/reflection.duckdb
  • Embeddings: Local ONNX models (all-MiniLM-L6-v2) for semantic search
  • Vector Storage: FLOAT[384] arrays for similarity matching
  • No External Dependencies: Everything runs locally for privacy
  • Cross-Project History: Conversations tagged by project context

Search Capabilities

  • Semantic Search: Vector similarity with customizable thresholds
  • Text Fallback: Standard text search when embeddings unavailable
  • Time Decay: Recent conversations prioritized in results
  • Project Context: Filter searches by project or search across all
  • Batch Operations: Efficient bulk storage and retrieval

Data Storage

This server manages its data locally in the user's home directory:

  • Memory Storage: ~/.claude/data/reflection.duckdb
  • Session Logs: ~/.claude/logs/
  • Configuration: Uses pyproject.toml and environment variables

Recommended Session Workflow

  1. Initialize Session: /session-mgmt:init

    • UV dependency synchronization
    • Project context analysis and health monitoring
    • Session quality tracking setup
    • Memory system initialization
    • Permission system setup
  2. Monitor Progress: /session-mgmt:checkpoint (every 30-45 minutes)

    • Real-time quality scoring
    • Workflow optimization recommendations
    • Progress tracking and goal alignment
    • Automatic Git checkpoint commits
  3. Search Past Work: /session-mgmt:reflect_on_past

    • Semantic search through project history
    • Find relevant past conversations and solutions
    • Build on previous insights
  4. Store Important Insights: /session-mgmt:store_reflection

    • Capture key learnings and solutions
    • Tag insights for easy retrieval
    • Build project knowledge base
  5. End Session: /session-mgmt:end

    • Final quality assessment
    • Learning capture across categories
    • Session handoff file creation
    • Memory persistence and cleanup

Benefits

Comprehensive Coverage

  • Session Quality: Real-time monitoring and optimization
  • Memory Persistence: Cross-session conversation retention
  • Project Structure: Context-aware development workflows

Reduced Friction

  • Single Command Setup: One /session-mgmt:init sets up everything
  • Local Dependencies: No external API calls or services required
  • Intelligent Permissions: Reduces repeated permission prompts
  • Automated Workflows: Structured processes for common tasks

Enhanced Productivity

  • Quality Scoring: Guides session effectiveness
  • Built-in Memory: Enables building on past work automatically
  • Project Templates: Accelerates development setup
  • Knowledge Persistence: Maintains context across sessions

Troubleshooting

Common Issues

  • Memory not working: Install optional dependencies with pip install "session-mgmt-mcp[embeddings]"
  • Path errors: Ensure cwd and PYTHONPATH are set correctly in .mcp.json
  • Permission issues: Use /session-mgmt:permissions to trust operations
  • Project context: Analyze current project health and structure

Debug Mode

# Run with verbose logging
PYTHONPATH=/path/to/session-mgmt-mcp python -m session_mgmt_mcp.server --debug

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