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7 days ago
pulsemcp

Bayesian Monte Carlo Tree Search

Provides a Bayesian Monte Carlo Tree Search engine for systematic exploration of different angles and interpretations, balancing exploration versus exploitation through probabilistic approaches that evolve through multiple iterations.

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// key findings
⚠️
Tool descriptions don’t match what handlers do
2 tools describe read intent but their handlers mutate — list_ollama_models (line 383: result = subprocess.run(['olloma', 'list'], capture_output=True, text=True, check=True)); list_ollama_models (line 384: result = subprocess.run(cmd, capture_output=True, text=True, check=False))
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
🔐
You'll be asked for 4 credentials: GEMINI_API_KEY, GOOGLE_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY
These are read from process.env at runtime. Make sure you trust where they’ll be sent.
// required environment variables
This server reads these from process.env. You'll be asked to provide them before it can run.
🔐 secretGEMINI_API_KEYyour-gemini-api-key-here
🔐 secretGOOGLE_API_KEY
configDEFAULT_LLM_PROVIDER
configDEFAULT_MODEL_NAMEgemini-2.0-flash
🔐 secretOPENAI_API_KEYsk-your-openai-key-here
🔐 secretANTHROPIC_API_KEYsk-ant-your-anthropic-key-here
configOLLAMA_HOST
// full audit trail
The full breakdown of what we checked, the deductions that landed, the network hosts, the dependency advisories, and concrete fix guidance is available to verified publishers.
// improvement guidance — verified publishers only
We have 7 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
[![M8ven Score](https://m8ven.ai/badge/mcp/angrysky56-mcts-mcp-server-fia6q7)](https://m8ven.ai/mcp/angrysky56-mcts-mcp-server-fia6q7)
commit: 59478284170bfdc6e5c91342f9bd97b50a51edc6
code hash: 28f90bded5297768451b54ab051f205ce5b97e435da0f2ab5b388d6a3b48e178
verified: 7/24/2026, 8:50:55 AM
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