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| Metadata-Version: 2.4 | |
| Name: openproblems-spatial-mcp | |
| Version: 0.1.0 | |
| Summary: Model Context Protocol server for OpenProblems spatial transcriptomics workflows | |
| Author: OpenProblems MCP Contributors | |
| License: MIT | |
| Project-URL: Homepage, https://github.com/openproblems-bio/SpatialAI_MCP | |
| Project-URL: Documentation, https://github.com/openproblems-bio/SpatialAI_MCP/docs | |
| Project-URL: Repository, https://github.com/openproblems-bio/SpatialAI_MCP | |
| Project-URL: Issues, https://github.com/openproblems-bio/SpatialAI_MCP/issues | |
| Keywords: mcp,model-context-protocol,spatial-transcriptomics,bioinformatics,nextflow,viash,docker,openproblems | |
| Classifier: Development Status :: 3 - Alpha | |
| Classifier: Intended Audience :: Science/Research | |
| Classifier: License :: OSI Approved :: MIT License | |
| Classifier: Programming Language :: Python :: 3 | |
| Classifier: Programming Language :: Python :: 3.8 | |
| Classifier: Programming Language :: Python :: 3.9 | |
| Classifier: Programming Language :: Python :: 3.10 | |
| Classifier: Programming Language :: Python :: 3.11 | |
| Classifier: Topic :: Scientific/Engineering :: Bio-Informatics | |
| Requires-Python: >=3.8 | |
| Description-Content-Type: text/markdown | |
| License-File: LICENSE | |
| Requires-Dist: mcp>=1.9.2 | |
| Requires-Dist: pyyaml>=6.0 | |
| Requires-Dist: requests>=2.31.0 | |
| Requires-Dist: click>=8.1.0 | |
| Requires-Dist: pandas>=2.0.0 | |
| Requires-Dist: numpy>=1.24.0 | |
| Requires-Dist: docker>=6.0.0 | |
| Requires-Dist: rich>=13.0.0 | |
| Provides-Extra: dev | |
| Requires-Dist: pytest>=7.0.0; extra == "dev" | |
| Requires-Dist: pytest-asyncio>=0.21.0; extra == "dev" | |
| Requires-Dist: black>=23.0.0; extra == "dev" | |
| Requires-Dist: flake8>=6.0.0; extra == "dev" | |
| Requires-Dist: mypy>=1.0.0; extra == "dev" | |
| Provides-Extra: docs | |
| Requires-Dist: mkdocs>=1.4.0; extra == "docs" | |
| Requires-Dist: mkdocs-material>=9.0.0; extra == "docs" | |
| Requires-Dist: mkdocs-mermaid2-plugin>=0.6.0; extra == "docs" | |
| Dynamic: license-file | |
| # SpatialAI_MCP | |
| Empowering spatial transcriptomics research by providing AI agents with a standardized interface to Nextflow pipelines, Viash components, and comprehensive documentation, accelerating discovery in the OpenProblems project. | |
| # OpenProblems Spatial Transcriptomics MCP Server | |
| ## Project Overview | |
| The OpenProblems Spatial Transcriptomics Model Context Protocol (MCP) Server is an initiative to enhance the efficiency, reproducibility, and accessibility of spatial transcriptomics research within the broader OpenProblems project. Our goal is to bridge the gap between cutting-edge biological methods and the computational infrastructure required to implement them, empowering bioinformaticians and AI agents alike. | |
| ## The Challenge in Spatial Transcriptomics Research | |
| Computational biology researchers, particularly in spatial transcriptomics, are primarily focused on developing novel scientific methods. However, the underlying computational infrastructure and auxiliary tools often present significant bottlenecks, diverting valuable scientific attention. Key challenges include: | |
| * **Massive Datasets:** Spatial transcriptomics data can be 10 to 100 times larger than single-cell RNA sequencing data, often reaching terabytes per experiment, requiring substantial computational resources.[1, 2, 3] | |
| * **Reproducibility Issues:** The field lacks universally accepted computational pipelines, and many custom-built workflows have minimal documentation, making reliable replication difficult.[1, 2] | |
| * **Tool Complexity:** Existing software tools are often not designed for the scale and intricacy of spatial transcriptomics data, necessitating significant manual effort for testing and validation.[3] | |
| * **Skill Gaps:** Spatial transcriptomics demands expertise in both image processing and computational biology, creating a skills gap.[1, 2] | |
| ## Our Solution: The OpenProblems Spatial Transcriptomics MCP Server | |
| We are building a Model Context Protocol (MCP) server that will serve as a central, standardized interface for AI agents to interact with Nextflow pipelines, single-cell and spatial transcriptomics data processing methods, and Dockerized workflows managed by Viash. This server will abstract away the complexities of auxiliary tools and frameworks, allowing bioinformaticians to focus on scientific innovation. | |
| The MCP, an open standard, enables Large Language Models (LLMs) and other AI applications to dynamically interact with external tools and data sources through a structured interface.[4, 5, 6] By leveraging MCP, we aim to transform AI agents into "Cognitive Accelerators" for spatial transcriptomics, enabling them to operate at a higher, more conceptual level within bioinformatics.[7] | |
| ## Project Goals and Key Impact Areas | |
| The MCP server will address critical needs within the OpenProblems project by providing: | |
| 1. **Centralized and Contextualized Documentation:** | |
| * **Goal:** To provide comprehensive, machine-readable documentation for Docker, Viash, Nextflow, and specific OpenProblems tools and pipelines. | |
| * **Impact:** This transforms static documentation into a computable "knowledge graph," enabling AI agents to understand tool relationships, parameters, and best practices, thereby enhancing context for coding agents.[4, 8, 9] | |
| 2. **Empowering Context-Aware AI Coding Agents:** | |
| * **Goal:** To enable AI coding agents to generate high-quality, DSL2-compliant Nextflow code, precise Viash component configurations, and optimized Dockerfiles. | |
| * **Impact:** AI agents will have direct access to structured schemas and best practices, significantly reducing debugging and validation efforts for human researchers.[10, 11] | |
| 3. **Enforcing Best Practices and Standardized Guidelines:** | |
| * **Goal:** To ensure all interactions and generated components adhere to predefined standards for reproducibility, scalability, and maintainability. | |
| * **Impact:** The MCP server will act as a central enforcer of best practices for Dockerfile optimization, Nextflow resource tuning, and Viash modularity, aligning with OpenProblems' benchmarking mission.[12, 13] | |
| 4. **Providing Curated Examples and Reusable Pipeline Templates:** | |
| * **Goal:** To expose a meticulously curated library of Nextflow pipeline templates (e.g., for spatial transcriptomics processing, spatially variable gene identification, label transfer) and Viash component examples. | |
| * **Impact:** Researchers and AI agents can rapidly prototype new workflows, accelerating development cycles and ensuring consistency across projects.[13, 14, 15] | |
| 5. **Facilitating Comprehensive Implementation Checklists:** | |
| * **Goal:** To provide AI agents with direct access to structured implementation checklists for systematic setup, configuration, and deployment of new workflows or components. | |
| * **Impact:** Checklists can be dynamically updated and validated by AI agents, ensuring strict adherence to evolving OpenProblems standards and minimizing human error in complex procedures. | |
| 6. **Streamlining Testing and Advanced Troubleshooting:** | |
| * **Goal:** To expose specialized "Tools" for automated testing (e.g., `nf-test` scripts, Viash unit tests) and advanced troubleshooting (e.g., analyzing Nextflow logs for actionable insights, identifying common errors like Out-Of-Memory issues). | |
| * **Impact:** This enables AI-driven "Proactive Troubleshooting" and "Test-Driven Workflow Development," significantly enhancing the robustness and reliability of bioinformatics workflows by automating error detection and resolution.[16, 17, 18, 19, 10, 20, 21] | |
| ## Technology Stack | |
| * **Model Context Protocol (MCP):** The core communication standard for AI-tool interaction.[4, 5, 6] | |
| * **Nextflow:** A robust framework for scalable and reproducible pipeline orchestration.[22, 23, 18, 24, 25] | |
| * **Viash:** A meta-framework for modularizing, standardizing, and generating Dockerized bioinformatics components.[18, 12, 26, 19, 13] | |
| * **Docker:** For ensuring consistent and portable computational environments.[27, 28, 29, 30] | |
| * **Python:** Primary language for MCP server implementation. | |
| ## Contribution | |
| The OpenProblems project is a community-guided benchmarking platform.[31] We welcome contributions from bioinformaticians, computational biologists, and AI developers. Please refer to our `CONTRIBUTING.md` for guidelines on how to get involved. | |
| ## Links | |
| * **OpenProblems Project:** | |
| * **OpenProblems `task_ist_preprocessing`:** | |
| * **OpenPipelines-bio:** | |
| * **Data Intuitive (Viash):** | |