Academic figure agent harness for multi-step planning, generation, and evaluation through MCP
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# 🎨 Academic Figures MCP [](https://pypi.org/project/academic-figures-mcp/) [](https://marketplace.visualstudio.com/items?itemName=u9401066.academic-figures-mcp) [](https://github.com/u9401066/academic-figures-mcp/actions/workflows/ci.yml) [](LICENSE) [](https://pypi.org/project/academic-figures-mcp/) **A multi-step academic figure agent harness for AI agents and non-engineers.** Academic Figures MCP is a workflow harness for multi-step academic reasoning and figure production. PMID ingestion is one structured entry point, but the real product value is helping an agent move through academic planning, concept decomposition, figure-type selection, prompt orchestration, image generation, evaluation, and iteration until it reaches a publication-grade result. Preprints, repositories, and freeform briefs are also first-class planning inputs. MCP exposure and VSX packaging make that workflow usable without requiring engineering-heavy setup. ## One-Click Install (VS Code) > Requires [uv](https://docs.astral.sh/uv/getting-started/installation/). The install shape uses `uvx --from academic-figures-mcp afm-server`, which is shell-neutral across macOS, Linux, and Windows. [](vscode:mcp/install?%7B%22name%22%3A%22academic-figures%22%2C%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22--from%22%2C%22academic-figures-mcp%22%2C%22afm-server%22%5D%7D) [![Install in VS Code Insiders](https://img.shields.io/badge/VS%20Code%20Insiders-Install…
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