Kiro Memory MCP server for AI agents

The Enhanced MCP Memory server is a powerful tool that extends AI assistants with intelligent memory management, task tracking, and project awareness capabilities. It provides semantic search, automatic task extraction, knowledge graphs, and comprehensive project management through a Model Context Protocol (MCP) server interface.

Installation

You can install and run the Enhanced MCP Memory server using one of three methods:

Using uvx (Recommended)

# Install and run with uvx
uvx enhanced-mcp-memory

Manual Installation

# Clone and install
git clone https://github.com/cbunting99/enhanced-mcp-memory.git
cd enhanced-mcp-memory
pip install -e .

# Run the server
enhanced-mcp-memory

Development Setup

# Clone repository
git clone https://github.com/cbunting99/enhanced-mcp-memory.git
cd enhanced-mcp-memory

# Install dependencies
pip install -r requirements.txt

# Run directly
python mcp_server_enhanced.py

MCP Configuration

To connect your MCP client to the Enhanced MCP Memory server, add the following configuration:

For uvx installation:

{
  "mcpServers": {
    "memory-manager": {
      "command": "uvx",
      "args": ["enhanced-mcp-memory"],
      "env": {
        "LOG_LEVEL": "INFO",
        "MAX_MEMORY_ITEMS": "1000",
        "ENABLE_AUTO_CLEANUP": "true"
      }
    }
  }
}

For local installation:

{
  "mcpServers": {
    "memory-manager": {
      "command": "python",
      "args": ["mcp_server_enhanced.py"],
      "cwd": "/path/to/enhanced-mcp-memory",
      "env": {
        "LOG_LEVEL": "INFO",
        "MAX_MEMORY_ITEMS": "1000",
        "ENABLE_AUTO_CLEANUP": "true"
      }
    }
  }
}

Available Tools

The Enhanced MCP Memory server provides various tools organized into different categories:

Core Memory Tools

get_memory_context(query) - Get relevant memories and context
create_task(title, description, priority, category) - Create new tasks
get_tasks(status, limit) - Retrieve tasks with filtering
get_project_summary() - Get comprehensive project overview

Sequential Thinking Tools

start_thinking_chain(objective) - Begin structured reasoning process
add_thinking_step(chain_id, stage, title, content, reasoning) - Add reasoning steps
get_thinking_chain(chain_id) - Retrieve complete thinking chain
list_thinking_chains(limit) - List recent thinking chains

Context Management Tools

create_context_summary(content, key_points, decisions, actions) - Compress context for token optimization
start_new_chat_session(title, objective, continue_from) - Begin new conversation with optional continuation
consolidate_current_session() - Compress current session for handoff
get_optimized_context(max_tokens) - Get token-optimized context
estimate_token_usage(text) - Estimate token count for planning

Enterprise Auto-Processing

auto_process_conversation(content, interaction_type) - Extract memories and tasks automatically
decompose_task(prompt) - Break complex tasks into subtasks

Project Convention Tools

auto_learn_project_conventions(project_path) - Automatically detect and learn project patterns
get_project_conventions_summary() - Get formatted summary of learned conventions
suggest_correct_command(user_command) - Suggest project-appropriate command corrections
remember_project_pattern(pattern_type, pattern, description) - Manually store project patterns
update_memory_context() - Refresh memory context with latest project conventions

System Management Tools

health_check() - Check server health and connectivity
get_performance_stats() - Get detailed performance metrics
cleanup_old_data(days_old) - Clean up old memories and tasks
optimize_memories() - Remove duplicates and optimize storage
get_database_stats() - Get comprehensive database statistics

Project Convention Learning

The server intelligently learns project-specific conventions to provide better assistance:

Automatic Detection

Operating System: Windows vs Unix, preferred shell and commands
Project Type: Node.js, Python, Rust, Go, Java, MCP servers, FastAPI, Django
Development Tools: IDEs, linters, formatters, CI/CD configurations
Package Management: npm, yarn, pip, poetry, cargo, go modules
Build Systems: Vite, Webpack, Make, batch scripts, shell scripts

Smart Command Suggestions

# Instead of generic commands, suggests project-specific ones:
User types: "node server.js"
AI suggests: "Use 'npm run dev' instead for this project"

User types: "python main.py" 
AI suggests: "Use 'uvicorn main:app --reload' for this FastAPI project"

Windows Optimization

Automatically detects Windows environment
Uses cmd.exe and Windows-appropriate path separators
Suggests Windows-compatible commands (e.g., dir instead of ls)
Handles Windows-specific Python and Node.js patterns

Configuration Options

Configure the server via environment variables:

Variable	Default	Description
`LOG_LEVEL`	`INFO`	Logging level (DEBUG, INFO, WARNING, ERROR)
`MAX_MEMORY_ITEMS`	`1000`	Maximum memories per project
`MAX_CONTEXT_TOKENS`	`8000`	Token threshold for auto-compression
`CLEANUP_INTERVAL_HOURS`	`24`	Auto-cleanup interval
`ENABLE_AUTO_CLEANUP`	`true`	Enable automatic cleanup
`MAX_CONCURRENT_REQUESTS`	`5`	Max concurrent requests
`REQUEST_TIMEOUT`	`30`	Request timeout in seconds
`DATA_DIR`	~/ClaudeMemory	Where to store data and logs

Performance & Monitoring

The server includes built-in performance tracking that you can access using:

get_performance_stats() - Get detailed performance metrics
health_check() - Check server health and connectivity

The tracking includes:

Response time monitoring for all tools
Success rate tracking with error counts
Memory usage statistics
Database performance metrics

Semantic Search

The server uses sentence-transformers for intelligent memory retrieval:

Natural language queries like "Find memories about database optimization"
Similarity-based matching using embeddings
Configurable similarity thresholds
Automatic model downloading (~90MB on first run)

Sequential Thinking

The server implements a structured reasoning system:

5-stage thinking process: Analysis → Planning → Execution → Validation → Reflection
Token optimization with real-time estimation and compression (30-70% reduction)
Context continuity with intelligent session handoffs
Auto-extraction of key points, decisions, and action items

How to install this MCP server

For Claude Code

To add this MCP server to Claude Code, run this command in your terminal:

claude mcp add-json "memory-manager" '{"command":"uvx","args":["kiro-mcp-memory"],"env":{"LOG_LEVEL":"INFO","MAX_MEMORY_ITEMS":"1000","ENABLE_AUTO_CLEANUP":"true"}}'

See the official Claude Code MCP documentation for more details.

For Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > Tools & Integrations and click "New MCP Server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "memory-manager": {
            "command": "uvx",
            "args": [
                "kiro-mcp-memory"
            ],
            "env": {
                "LOG_LEVEL": "INFO",
                "MAX_MEMORY_ITEMS": "1000",
                "ENABLE_AUTO_CLEANUP": "true"
            }
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explicitly ask the agent to use the tool by mentioning the tool name and describing what the function does.

For Claude Desktop

To add this MCP server to Claude Desktop:

1. Find your configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

2. Add this to your configuration file:

{
    "mcpServers": {
        "memory-manager": {
            "command": "uvx",
            "args": [
                "kiro-mcp-memory"
            ],
            "env": {
                "LOG_LEVEL": "INFO",
                "MAX_MEMORY_ITEMS": "1000",
                "ENABLE_AUTO_CLEANUP": "true"
            }
        }
    }
}

3. Restart Claude Desktop for the changes to take effect