Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Multimodal Information Synthesis for AI-Assisted Agricultural Field Assessment Using MCP Get the yield prediction from the latest field image and summarize the soil moisture data for that field."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Stefan Stiller
Leibniz-Centre for Agricultural Landscape Research (ZALF) e.V.
Email: stefan [dot] stiller [at] zalf [dot] de, stillsen [at] gmail [dot] com
ORCID: 0009-0004-7468-1678
Related MCP server: agribrain
This repository accompanies the study "Multimodal Information Synthesis for AI-Assisted Agricultural Field Assessment Using Model Context Protocol".
It provides software for structured multimodal information access for large language models (LLMs) via the Model Context Protocol (MCP):
MCP resource servers that expose agricultural information modalities to LLM clients:
yield prediction from RGB imagery
scientific PDF literature (topic-based access)
soil CSV data (lab results and moisture)
Evaluation plotting code that regenerates the manuscript figures comparing:
structured MCP access vs unstructured availability of the same sources
increasing modality dose under MCP (single resources → pairs → all resources)
Related work on the underlying yield model is available at:
Self-Supervised Learning for Crop Classification and Yield Prediction
.
├── MCP_yield_server.py # MCP server: image-based yield prediction
├── MCP_pdf_resource_server.py # MCP server: topic-based PDF literature access
├── MCP_csv_resource_server.py # MCP server: soil CSV resources
├── llm_judge-two-question-figures.py # Manuscript Fig. 1 and Fig. 2
├── requirements.txt
├── CITATION.cff
├── LICENSE # GNU GPLv3
└── README.mdStart each server from an MCP-compatible client configuration (e.g. Claude Desktop / Cursor), pointing to the corresponding Python file:
MCP_yield_server.py
MCP_pdf_resource_server.py
MCP_csv_resource_server.py
python llm_judge-two-question-figures.pyThis writes Figure 1 (structured vs unstructured access) and Figure 2 (MCP modality ladder), plus caption text files, to Evaluation_Reports/.
Judge model names in the figures are remapped for display; scores come from the evaluation CSV referenced in the script.
Edit OUTPUT_DIR / CSV_NAME in the figure script if your layout differs.
If you use this code, please cite it as indicated in CITATION.cff (and the associated paper when available).
This project is licensed under the GNU GPLv3 License — see the LICENSE file for details.
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