norika1207-lab/mercury-mcp
View on GitHub ↗Cross-architecture LLM internal observation database (23 models, 13 architecture families). Exposed as MCP tools for any AI coding agent.
43 ★14 forksPythonUpdated 3mo ago
What you need to know
MCP server exposing a cross-architecture LLM internal observation database (23 models, 13 architecture families) as agent tools for mechanistic interpretability.
Install
git clone && pip install -e .
Key features
- ✓7 tools: list_models, anchor_dims, universal_anchors, layer_fingerprint, cross_arch_equivalent, compose_recipe, about
- ✓Tier-A output-layer logit hooks and Tier-B residual stream fingerprints
- ✓Cross-architecture layer alignment data
- ✓Built on consumer hardware (Mac mini M4 Pro)
Best for
Mechanistic interpretability researchers and model-merge practitioners.
Platforms: Python
Reviewed 2026-08-11
Topics
ai-agentsanchor-dimensionsconsumer-hardwarecross-architecturefrankenstein-mergellmmcpmechanistic-interpretabilitymodel-context-protocolopen-datatransformer
- Stars
- 43★
- Forks
- 14
- Language
- Python
- License
- —
- Created
- 2026-05-23
- Last push
- 2026-05-24