"Resources for Interacting with Blockchains Like Ethereum" matching MCP servers:

Enables AI agents to query free biomedical and pharmaceutical APIs for clinical trials, drug data, molecular structures, adverse events, and research literature.

Provides oncology data (research, news, FDA approvals, clinical trials) and IBM MAMMAL biomedical predictions (PPI, DTI, ClinTox) as MCP tools. Allows AI agents to query cancer datasets and run predictions through a remote MCP server.

Exposes four CMS public datasets as callable tools in Claude conversations, enabling natural-language real-world evidence analytics without SQL expertise.

This server enables interacting with the National Digital Health Mission's Health Information User (HIU) API, allowing agents to access and manage health information through the Multi-Agent Conversation Protocol.

Enables searching the NIAID Data Ecosystem for biomedical research resources including datasets, clinical studies, and publications using Elasticsearch queries.

Phylogenetic inference server driving IQ-TREE 2, always returning per-clade bootstrap support plus model selection, tree comparison, and alignment simulation, with detailed diagnostics like conflicting clades and model uncertainty.

A Model Context Protocol (MCP) server for interacting with Gen3 data commons, with GraphQL query validation to reduce hallucinations.

Renders interactive 2D molecular structure diagrams from SMILES notation and computes molecular properties like molecular weight, LogP, and TPSA, directly in the chat.

A local MCP server enabling PubMed, PMC, and iCite API access via stdio for tools like search, fetch, full text, and citation counts.

Typed, validated FHIR tools for healthcare AI agents — build, read, validate, and code FHIR resources from a patient bundle, with terminology lookup and machine-readable validation reports built for fix-and-retry.

Enables LLM-based agents to interact with FHIR healthcare data through natural language prompts, providing full CRUD operations on FHIR resources, document processing, and semantic search capabilities.

Enables conversational access to 400,000+ clinical trials on ClinicalTrials.gov, allowing users to search, compare, and retrieve trial details using plain English through MCP-compatible clients like Cursor and Claude Desktop.

Enables natural language querying of Korean public medical data (data.go.kr) through MCP clients like Claude, providing tools for searching hospitals, drugs, non-payment costs, disease statistics, and more.

A TypeScript MCP server for interacting with CDISC Define-XML 2.0 and 2.1 files, enabling users to load, search, and retrieve clinical data metadata including datasets, variables, and codelists. It supports local file processing with document caching and provides responses in both JSON and Markdown formats.

Connects to wetlab protocol resources such as protocols.io, enabling users to access and manage experimental protocols via natural language.

A reference MCP server for clinic scheduling and intake, demonstrating production patterns like tenant isolation, idempotent writes, and structured errors using synthetic data.

An MCP server for rehabilitation monitoring that provides tools for patient risk scoring, outcome analysis, report generation, alerts, and research search, along with prompts and CSV data resources.

A Model Context Protocol server that provides tools for interacting with the STRING database to analyze protein-protein interaction networks and functional enrichment. It enables users to map protein identifiers, retrieve interaction data, and generate biological network visualizations through natural language interfaces.

Provides comprehensive integration with the Cliniko API for healthcare practice management, including patient, appointment, and invoice administration. It enables tools for complex clinical workflows and direct access to practice resources through the Model Context Protocol.

An MCP server that provides a toolbox for interacting with EPA SWMM stormwater models, enabling users to analyze model data and interpret results through LLM-driven tools. It assists stormwater modelers in understanding hydraulic systems and modeling behavior using natural language interfaces.