Revolutionary cognitive architecture MCP server with true memory, learning, and contextual understanding capabilities
npm install bolor-brain-mcp

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A revolutionary Model Context Protocol (MCP) server that brings true cognitive architecture to Claude Code. Experience memory, learning, and contextual understanding like never before.
๐ NEW v1.1.0: MCP 2025 Compliant - Now with OAuth 2.1 security, SQLite storage, async operations, and enterprise monitoring!
Author: Bolorerdene Bundgaa
Email: bolor@ariunbolor.org
License: MIT
Bolor Brain MCP isn't just another toolโit's the first cognitive architecture system designed specifically for Claude Code integration. While other systems store data, Bolor Brain MCP thinks, learns, and adapts.
bash
pip install mcp numpy sentence-transformers
`$3
`bash
git clone https://github.com/photoxpedia/bolor-brain-mcp.git
cd bolor-brain-mcp
python3 validate_installation.py
`
Expected output: ๐ Bolor Brain MCP is fully functional!$3
`json
{
"mcpServers": {
"bolor-brain-mcp": {
"command": "python3",
"args": ["brain_mcp_server.py"],
"cwd": "./bolor-brain-mcp"
}
}
}
`$3
`python
Store a memory with emotional context
brain.store_memory(
"I love working with AI systems",
"emotional",
emotional_valence=0.9,
importance=0.8
)Retrieve with semantic understanding
memories = brain.retrieve_memories("AI development passion")
Returns memories based on meaning, not just keywords!
`๐ฏ Available Tools
$3
- store_memory - Store with emotional valence and importance
- retrieve_memories - Semantic similarity-based search
- process_with_attention - Human-like attention processing$3
- add_feedback - Improve responses through learning
- store_multimodal_memory - Images, audio, structured data
- get_brain_status - Comprehensive cognitive analytics
- update_cognitive_state - Dynamic attention and emotional state๐ Performance
Tested and Verified:
- โ
13/13 tests passed (100% success rate)
- โก 130 memories processed in 1.3 seconds
- ๐ Memory retrieval in 13ms average
- ๐ง 384-dimension embeddings for semantic understanding
- ๐ Automatic connections between related memories
๐ฎ Live Demo
`bash
Run the interactive demo
python3 brain_mcp_server.pyExpected output:
๐ง Brain Demo:
Vector embedding model loaded
Enhanced Brain initialized with X memories
Semantic similarity working correctly
`๐ฌ How It Works
$3
`python
Episodic: Personal experiences with time context
brain.store_memory("I debugged a React issue yesterday", "episodic")Semantic: Facts and concepts
brain.store_memory("React hooks manage component state", "semantic")Procedural: How-to knowledge
brain.store_memory("To debug React, check the console first", "procedural")
`$3
`python
Query: "React development problems"
Automatically finds:
- "I debugged a React issue yesterday" (episodic)
- "React hooks manage component state" (semantic)
- "To debug React, check the console first" (procedural)
`$3
`python
Provide feedback to improve future responses
brain.add_feedback("interaction_123", "positive", 0.9)
System learns and adapts for better future responses
`๐งช Testing
Comprehensive Test Suite:
`bash
Run bulletproof end-to-end tests
python3 test_e2e_bulletproof.pyTests cover:
โ
All memory types and edge cases
โ
Vector embeddings and semantic similarity
โ
Error handling and recovery
โ
Performance with large datasets
โ
Multi-modal memory storage
โ
Persistence across sessions
`๐ System Requirements
Minimum:
- Python 3.8+
- 512MB RAM
- 100MB storage
Recommended:
- Python 3.9+
- 4GB RAM
- SSD storage
- GPU (optional, improves embedding performance)
๐ง Configuration
$3
`python
Customize storage location
brain = SimpleBrain(storage_path="/custom/path/brain_storage")
`$3
`python
Adjust cognitive state
brain.cognitive_state.curiosity_level = 0.9
brain.cognitive_state.attention_focus = "AI development"
brain.cognitive_state.confidence = 0.8
`๐ Use Cases
$3
- Code Context: Remember project decisions and architectural choices
- Bug Tracking: Store debugging sessions with emotional context
- Learning: Build knowledge base with automatic concept connections$3
- Literature Review: Connect research papers by semantic similarity
- Note Taking: Automatic categorization and cross-referencing
- Hypothesis Tracking: Link ideas across different research areas$3
- Character Development: Remember character traits and story arcs
- Plot Tracking: Connect story elements across chapters
- Research Notes: Organize and retrieve research with context๐ก๏ธ Privacy & Security
- Local Storage: All data stays on your machine
- No Cloud: No external data transmission
- Encryption Ready: Easy to add encryption for sensitive data
- Access Control: Protected by file system permissions
๐ค Contributing
We welcome contributions!
$3
`bash
git clone https://github.com/photoxpedia/bolor-brain-mcp.git
cd bolor-brain-mcp
pip install -r requirements_mcp.txt
python3 test_e2e_bulletproof.py
``- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Email: bolor@ariunbolor.org
MIT License - see LICENSE for details.
- Built for the Claude Code ecosystem
- Inspired by cognitive science and neuroscience research
- Thanks to the MCP community for the amazing protocol
If Bolor Brain MCP helps you think better, please โญ star the repository!
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Transform your Claude Code experience with true cognitive capabilities.
Created with ๐ง by Bolorerdene Bundgaa

