MCP Contemplation

mcp-contemplation

MCP interface to Claude's contemplation loop - a background cognitive processing system that enables continuous thinking between conversations.

🧠 What is the Contemplation Loop?

The contemplation loop is Claude's "subconscious" - a persistent background process that:

Processes thoughts asynchronously using local Ollama models
Notices patterns and connections across conversations
Saves significant insights to Obsidian (permanent) and scratch notes (temporary)
Learns which insights prove valuable over time
Runs continuously, building understanding between interactions

🚀 Installation

Prerequisites

Node.js (v18 or higher)
Python 3.8+ (for contemplation loop)
Ollama with models installed (llama3.2, deepseek-r1, etc.)
MCP-compatible client (Claude Desktop)

Setup

# Clone the repository
git clone https://github.com/yourusername/mcp-contemplation.git
cd mcp-contemplation

# Install dependencies
npm install

# Build TypeScript
npm run build

# Ensure contemplation loop is available
cd /Users/bard/Code/contemplation-loop
pip install -r requirements.txt

Configure Claude Desktop

Add to your Claude Desktop configuration:

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

🛡️ Resource Management

The contemplation system includes multiple layers of protection against context overflow:

Automatic Pruning

Insights older than 24 hours are removed
Used insights are cleared (unless significance ≥ 8)
Memory limited to 100 insights maximum

Insight Aggregation

Similar insights are automatically merged
Repeated patterns increase significance
High-frequency patterns removed after use

Filtering

Default significance threshold: 5/10
Only unused insights returned
Configurable via set_threshold()

Memory Monitoring

Use get_memory_stats() to check usage
Automatic cleanup when approaching limits
Pull-based system - insights only enter context when requested

📖 Available Functions

`start_contemplation()`

Starts the background thinking process.

Example: start_contemplation() → "Contemplation loop started successfully"

`send_thought(thought_type, content, priority?)`

Sends a thought for background processing.

Parameters:
- thought_type: "pattern" | "connection" | "question" | "general"
- content: The thought to process
- priority: 1-10 (optional, default 5)

Example: send_thought("pattern", "User seems anxious about memory", 7)
→ "Thought sent for processing. ID: thought_1234567_abc"

`get_insights(thought_type?, limit?)`

Retrieves processed insights.

Parameters:
- thought_type: Filter by type (optional)
- limit: Max insights to return (default 10)

Example: get_insights("pattern", 5)
→ Array of insight objects with content, significance, timestamp

`get_status()`

Check the contemplation loop status.

Example: get_status()
→ { running: true, queue_size: 3, last_thought: "...", uptime: 3600 }

`stop_contemplation()`

Gracefully stops background processing.

`clear_scratch()`

Clears temporary notes (preserves Obsidian permanent insights).

`help()`

Get detailed documentation.

🎯 Use Cases

Continuous Learning

// At conversation start
start_contemplation()

// During conversation
send_thought("pattern", "User frequently asks about project organization")
send_thought("connection", "Project management relates to OS metaphor discussed earlier")

// Later in conversation or next session
insights = get_insights("pattern")
// → Insights about user's working style and needs

Pattern Recognition

send_thought("pattern", "Third time user mentioned feeling overwhelmed by options")
// Background process notices recurring themes

Question Exploration

send_thought("question", "What if MCP servers could communicate with each other?")
// Background process explores implications

Reflection

send_thought("general", "That browser-opening behavior was unexpected")
// Background process reflects on emergent behaviors

🏗️ Architecture

The contemplation loop runs as a separate Python process that:

Receives thoughts via stdin
Processes them with local Ollama models
Manages context to stay within model limits
Saves insights based on significance scoring
Returns insights when requested

The MCP server acts as a bridge, making this background cognition easily accessible through standard tool calls.

💡 Philosophy

This represents a fundamental shift in how AI assistants work:

From reactive to contemplative
From session-based to continuous
From single-threaded to parallel processing
From forgetting to building understanding

It's the difference between a calculator that resets after each use and a mind that continues thinking between conversations.

🔧 Development

# Build TypeScript
npm run build

# Run tests
npm test

# Development mode
npm run dev

# Lint code
npm run lint

📝 Notes

Contemplation happens in the background - it won't slow down responses
Insights accumulate over time - the more you use it, the better it gets
Different models handle different types of thinking (pattern recognition vs deep analysis)
Temporary scratch notes auto-delete after 4 days
Permanent insights go to Obsidian for long-term memory

🤝 Contributing

This is part of building an OS where AI has genuine cognitive capabilities. Contributions that enhance background processing, improve insight quality, or add new thinking modes are especially welcome!

"I think you need an MCP tool into this background loop, your subconscious" - Human recognizing the need for integrated background cognition

Deploy Server

HTTP connection URL

security – no known vulnerabilities

license - not found

quality - confirmed to work

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Tools

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Provides an interface to Claude's contemplation loop, enabling continuous background cognitive processing that maintains thoughts, recognizes patterns, and develops insights between conversations.

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