Biomedical APIs MCP Server
Related Servers
Alternatives to Biomedical APIs MCP Server
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityDmaintenanceEnables AI agents to query and analyze FDA adverse events, drug labels, medical device clearances, and other public health datasets through natural language commands.14-
- FlicenseNot gradedqualityDmaintenanceEnables AI agents to access FDA and ClinicalTrials.gov data for medical device compliance, adverse event monitoring, and regulatory due diligence.-
- AlicenseNot gradedqualityDmaintenanceProvides real-time access to medical data including drug interactions, ICD-10 codes, FDA adverse event reports, and clinical guidelines. It enables LLMs to query databases like openFDA, PubMed, and CMS for pharmaceutical and clinical information.1MIT
- AlicenseNot gradedqualityDmaintenanceEnables drug safety, interactions, adverse events, and regulatory data queries using free sources like DrugBank, WHO, FDA FAERS, and more. Provides tools for checking drugs, interactions, adverse events, and searching across pharmaceutical databases.MIT
- AlicenseAqualityDmaintenanceEnables LLMs to search FDA drug labels and adverse event data via the OpenFDA API, supporting natural language queries for drug safety information.2MIT
- FlicenseNot gradedqualityBmaintenanceProvides a standardized interface for AI assistants to search biomedical literature and clinical trials, combining evidence from multiple sources and offering a prompt for structured research briefs.-
TDQS
Scored across 14 tools
Each tool targets a distinct biomedical database or data type, with descriptions clearly differentiating their sources and functions. There is no overlapping purpose among the 14 tools.
Tool names follow a consistent verb_noun pattern: 'get_' for direct retrieval, 'query_' for credentialed databases, and 'search_' for keyword queries. The prefixes are used consistently across the set.
With 14 tools covering diverse biomedical data sources (compounds, activities, trials, literature, adverse events, clinical datasets), the count is well-scoped for the server's purpose without being excessive or thin.
The tool set covers major biomedical data domains, including compounds, bioactivities, trials, literature, and clinical datasets. Minor gaps exist (e.g., protein data, gene expression), but core workflows are well-supported.