by norika1207-lab · MCP Server · ★ 24
Mercury MCP: Cross-Architecture LLM Internal Observation, as Agent Tools "Most AI coding agents don't know what's inside the model they're talking to. Mercury does." Mercury MCP exposes a 23-LLM cross-architecture observation database to any agent that speaks the Model Context Protocol (Claude Code, Cursor, Cline, Goose, etc.). Built entirely on consumer hardware (one Mac mini + one NVIDIA DGX Spark) at near-zero compute cost.
| Stars | 24 |
| Forks | 7 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 73.2517050925967/100 |
| Last Updated | 2026-05-24 |
| Created | 2026-05-23 |
| Platforms | mcp, python |
| Est. Tokens | ~2k |
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mercury-mcp is Cross-architecture LLM internal observation database (23 models, 13 architecture families). Exposed as MCP tools for any AI coding agent.. It is categorized as a MCP Server with 24 GitHub stars.
mercury-mcp is primarily written in Python. It covers topics such as ai-agents, anchor-dimensions, consumer-hardware.
You can find installation instructions and usage details in the mercury-mcp GitHub repository at github.com/norika1207-lab/mercury-mcp. The project has 24 stars and 7 forks, indicating an active community.