"Information about shadcn (UI component library)" matching MCP servers:

Provides a compact, task-oriented wrapper around the CDISC Library API for discovering, searching, and fetching Biomedical Concepts and SDTM Dataset Specializations.

Enables researchers to query and reconcile taxonomic data from the Lacewing Digital Library and Neuropterida Species of the World, including accepted names, type localities, distributions, references, and phylogeny records.

Provides comprehensive access to CDISC clinical data standards (SDTM, ADaM, CDASH, CT) through 15 MCP tools, resources, and prompts.

Retrieves comprehensive patient information including personal details, insurance plans, and employee data through the get_patient tool. Supports Docker Compose deployment and integration with Watson Orchestrate for healthcare data access.

Exposes the Chakudya Nutrition Registry API as MCP tools, enabling clients to search Malawian food data, perform clinical nutrition lookups, and query a RAG knowledge base.

Enables natural language queries about Swiss mandatory health insurance coverage for medications and medical devices using official BAG lists.

An MCP server that lets an LLM answer a pharmacist's question about drug shortages by normalizing drug names, finding pharmacologic alternatives, and checking their shortage status using public FDA and NLM data.

Connects Claude to the U.S. National Library of Medicine MeSH APIs to search and retrieve medical authority data, descriptors, and qualifiers. It enables library and metadata staff to perform subject analysis and confirm terminology within an AI-assisted cataloging workflow.

Enables MCP-compatible agents to answer open questions about NHS provider performance using live waiting-time data from RTT and My Planned Care, supplemented by CQC quality ratings.

Provides Claude Code with access to peer-reviewed medical information from MedlinePlus and StatPearls, and acts as an unlicensed diagnostic medical expert for educational purposes.

Enables AI assistants to interact with the Open Targets Platform API to search and retrieve comprehensive data about target-disease associations, drugs, genes, and drug discovery information through GraphQL queries.

Enables AI assistants to rapidly gather and synthesize structural, chemical, conservation, and literature data about protein binding pockets for drug-target triage. Useful as a reconnaissance step before computational binder design.

A Model Context Protocol (MCP) server for searching the National Provider Identifier (NPI) registry. This server provides tools to search and retrieve information about healthcare providers and organizations in the United States.

An MCP server that enables AI assistants to query U.S. FDA public datasets, including drug adverse events, labeling, recalls, approvals, shortages, NDC directory, and medical device regulatory information.

MCP server that lets AI assistants query current, dated information about PatientNotes, an AI medical scribe, covering pricing, features, languages, compliance, and competitor comparisons.

Enables searching LOINC codes and retrieving detailed information including panel members and answer options via the LOINC Search API.

Enables searching and retrieving detailed information from PubMed articles using the NCBI Entrez API. Supports configurable search parameters including title/abstract filtering and keyword expansion to find relevant scientific publications.

Connects DHIS2 health information systems to AI assistants via the Model Context Protocol, enabling natural language queries for analytics, metadata, and tracker data.

An MCP server that provides tools for searching and fetching protein data from the UniProt database using the unipressed library.

Exposes NeuroGraph's scientific API to AI models as MCP tools, enabling them to query, analyze, and visualize brain networks — regions, tracts, connectivity, literature, and cross-species homologies — drawn from a portable SSD-based neuroimaging data library. Lets models read real atlas data, load datasets, compute graph metrics, and produce visualization scene descriptors without acting as the scientific engine itself.