An MCP server that provides direct access to PubMed and PubMed Central via the NCBI E-utilities API. It enables AI models to search biomedical literature, retrieve detailed article metadata, and download open-access full texts.
Enables agents to search the main academic literature APIs (arXiv, Crossref, Semantic Scholar) and the web via Tavily, and to extract full text from PDFs or web pages, returning each source's own native metadata fields.
Enables biomedical research workflows via MCP, including experiment and document search, PDF text extraction, evidence retrieval, and validation with provenance tracking.
Exposes NCBI PubMed as MCP tools for searching literature, fetching abstracts, exploring citation graphs, and finding author publications without requiring an API key.
MCP server for accessing NCBI PubMed/PMC databases, enabling search, retrieval of summaries and full records, citation export, and ID conversion through various APIs.
Enables agents to search a public PubMed-derived corpus by natural language or identifiers and to retrieve publication details while traversing bounded citation and semantic literature graphs.
A lightweight MCP server for clinical biomedical literature retrieval, enabling PubMed search, article metadata, full-text access, and evidence summarization through MCP-compatible clients.
This MCP server provides 16 intelligent tools for searching, retrieving, and linking biomedical literature from PubMed and PMC. It enables LLM applications to perform complex queries, batch processing, and cross-database linking.
A bridge connecting AI agents to NCBI's PubMed database through the Model Context Protocol, enabling seamless searching, retrieval, and analysis of biomedical literature and data.
Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.
Enables structured learning with a verified loop: define goals as observable claims, learn through teach-lab-test-gate per claim, and get independently graded by an adversarial examiner to ensure genuine progress.
Exposes a local biomedical literature pipeline as MCP tools for automated research workflows. Enables literature search, open-access paper retrieval, and draft generation for biomedical and pathology domains through standard MCP clients.