Mercury MCP

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作者：norika1207-lab

Cross-architecture LLM internal observation database (23 models, 13 architecture families). Exposed as MCP tools for any AI coding agent.

ai-agentsanchor-dimensionsconsumer-hardwarecross-architecturefrankenstein-mergellmmcpmechanistic-interpretabilitymodel-context-protocolopen-datatransformer

安装

$pip install -e .

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工具（17 个）

codestral

22B (mistral coder variant)

command-r

35B (Cohere)

falcon3

gemma2

granite

3.1-dense-8B (IBM)

internlm2

llama

3.1-8B, 3.3-70B

mercury_about

Paper status, citations, contribution info

mercury_list_models

List all 23 observed models

mistral

7B-v0.3, small-3.2-24B

olmo2

7B (AllenAI fully-open)

phi3

medium-14B

qwen2

3B, 7B, 14B, 32B, coder-32B

starcoder2

15B

Tier-A

hooked into llamacpp.Llama.logitsprocessor to capture output-layer hot dims per token

Tier-B

transformers.AutoModelForCausalLM.frompretrained with outputhiddenstates=True, per-layer residual stream activation magnitudes binned into 10 quantiles

yi

1.5-9B