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Retrieve papers, analyze datasets, scaffold projects, and manage directories via MCP.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-personal" yet — see the docs or source repo.
Please retrieve papers on graph neural networks for recommender systems and organize the results by topic.
A list of relevant papers organized by topic or research direction.
Please analyze this dataset’s basic structure, including field types, missing values, and initial observations.
A structural overview of the dataset with basic analysis results.
Please generate project scaffolding for a Python MCP service and organize the basic directory structure.
A basic project structure ready for further development and file organization.
Researchers or students can use it to retrieve relevant papers and organize the results before starting a project, reducing manual search time.
Data analysts can use it for initial dataset analysis when receiving new data, quickly understanding the structure and basic characteristics.
Developers can use its project scaffolding and directory utilities to quickly set up the foundation for a new tool or service.
It is a personal MCP server built with Python FastMCP that offers paper retrieval, dataset analysis, project scaffolding, and directory utilities.
The provided information says it supports both stdio and HTTP transport.
Yes, it supports optional bearer token authentication. For exact configuration details, see the source repository.
Use specialized MCP servers for data, documents, calendars, and prompt workflows.
Search papers, parse full-text PDFs, extract details, and manage citations.
Index and search paper collections across isolated local projects via MCP.
Auto-generate MCP servers so AI can query data sources without code.
Securely let AI read, write, and query local files and metadata.
Search official library docs and return clean text ready for LLM use.