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C
Limited view
73/100
2 months ago

Cerebro

A cognitive memory system for AI that provides persistent memory, learning, causal reasoning, and predictive intelligence through 49 MCP tools.

→ Professor-Low/Cerebro· listed on glama

Limited view. Automated analysis covers part of this stack. Findings reflect what we verified. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

Limited view: static analysis for Python is partially covered.

How we verified

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Who stands behind it

Professor-Low

Source: Glama

Is this your MCP?

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// key findings
✅
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.
// environment variables
To run this server yourself, you supply these values. They go in your own MCP client configuration and stay on your machine. The secret label means the value is sensitive, not that the server mishandles it.
configCEREBRO_DGX_HOST
configCEREBRO_NAS_IP
configENABLE_EMBEDDINGS
configAUTOSAVE_SESSION_ID
configAUTOSAVE_TRANSCRIPT_PATH
configAUTOSAVE_HOOK_EVENT
configCEREBRO_DATA_DIR~/.cerebro/data Base directory for all Cerebro data
configCEREBRO_STORAGE_PATH
configAI_MEMORY_PATH
configCEREBRO_NAS_PATH
configCEREBRO_EMBEDDING_MODELall-mpnet-base-v2 Sentence transformer model for semantic search
configCEREBRO_EMBEDDING_DIM768 Embedding vector dimensions
configCEREBRO_DEVICE
configCEREBRO_DGX_OLLAMA_PORT
configCEREBRO_DGX_EMBEDDING_PORT
configCEREBRO_DGX_SEARCH_PORT
configCEREBRO_LLM_URL(none) Optional local LLM endpoint for deeper reasoning
configCEREBRO_LLM_MODEL(none) Optional local LLM model name
configCEREBRO_PORT
configCEREBRO_HOST
configCEREBRO_LOG_LEVELINFO Logging level
configCEREBRO_VENV_PYTHON
configDGX_EMBEDDING_HOST
configDGX_EMBEDDING_PORT
configDGX_EMBEDDING_TIMEOUT
configDGX_SEARCH_HOST
configDGX_SEARCH_PORT
configDGX_SEARCH_TIMEOUT
configCEREBRO_LOCAL_BRAIN_PATH
configTF_CPP_MIN_LOG_LEVEL
// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

50/50 tools missing one or more hints — save_conversation_ultimate (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); search_knowledge_base (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_entity_info (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +47 more. OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

Tests exist

No test files found

Add tests that exercise each declared tool.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 3 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/professor-low-cerebro-1xmqps)](https://m8ven.ai/mcp/professor-low-cerebro-1xmqps)
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commit: 67be5ed12f486371430901d66f4befa6317e5683
code hash: 0476fe628fee4d52a750509b4b152d9008afe0f3352c3315531077a95d3edc3a
verified: 6/22/2026, 1:14:46 PM
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