"Resources for Learning Coding Library Documentation" matching MCP servers:

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

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.

Enables AI assistants to search and retrieve structured peptide research profiles with cited, tier-labelled evidence, safety and regulatory status, and to compare two or three peptides side by side without ranking. Also provides research article and FAQ search, category and trend lookups, plus unit conversion, solution concentration, blend and cost-per-amount calculators, all read-only and anonymous.

Enables users to train and compare Logistic Regression and KNN models, make breast-cancer predictions, retrieve sample inputs, compute confusion-matrix metrics, generate SHAP explanations, select the best model, and export Parquet audit logs through MCP tools.

Medicare claim documentation lookup for CPT codes against live CMS sources, returning structured requirements and document URIs with optional enrichment via RCI APIs.

Enables training, evaluating, and comparing Logistic Regression and KNN models on the Wisconsin breast cancer dataset, then predicting malignant or benign diagnoses from patient feature inputs and generating SHAP explanations for the selected model. Every tool call is logged to a Parquet audit trail that can be exported for review.

Enables AI assistants to access Medikode's medical coding platform for validating CPT/ICD-10 codes, performing chart quality assurance, parsing EOBs, calculating RAF scores, and extracting HCC codes from clinical documentation.

Enables training and comparing Logistic Regression and KNN models, generating predictions and SHAP explanations, selecting the best model, and exporting Parquet audit logs through MCP tools.

Enables users to train and compare breast-cancer classification models, obtain sample inputs, make predictions with Logistic Regression or KNN, evaluate metrics, select the best model, generate SHAP explanations, and export Parquet audit logs through MCP tools.

Enables training, predicting, evaluating, and explaining Logistic Regression and KNN models for breast cancer classification through MCP tools. It also supports model selection, SHAP explanations, and Parquet audit exports.

Enables users to train and evaluate Logistic Regression and KNN models for breast cancer diagnosis, generate predictions and SHAP explanations, select the best-performing model, and export Parquet audit logs through MCP tools.

Enables users to train, evaluate, and compare Logistic Regression and KNN breast-cancer models, generate predictions and SHAP explanations, select the best model, and export Parquet audit logs through MCP tools.

Enables training, evaluating, selecting, and explaining breast cancer prediction models through MCP tools for predictions, metrics, SHAP explanations, sample inputs, and Parquet audit exports.

Enables training and comparing Logistic Regression and KNN models, generating sample patient inputs, making malignant/benign predictions, evaluating confusion-matrix metrics, selecting the best model, producing SHAP explanations, and exporting Parquet audit logs through MCP tools.

Enables training and comparing logistic regression and KNN models, generating predictions and SHAP explanations, selecting a best model, and exporting Parquet audit logs through MCP tools.

Exposes MCP tools that train, evaluate, compare and explain Logistic Regression and KNN models on the Wisconsin breast cancer dataset, then serve malignant/benign predictions with SHAP explanations and a Parquet audit log of every call. It also provides ready-to-use sample patient inputs and model-selection tooling so agents can run a full ML workflow through natural language.

Enables training and comparing logistic regression and KNN models, retrieving sample patient inputs, generating predictions and confusion-matrix metrics, selecting the best model, producing SHAP explanations, and exporting Parquet audit logs through MCP tools.

Enables Alexa+ users to coordinate household pet care by logging feeding, medication, activity, litter, and health observations for dogs and cats, with evidence-grounded triage, Q&A, and daily briefings backed by a verified veterinary library and citations.

Enables scenario-based clinical communication practice through MCP tools for discovering scenarios, starting sessions, sending practitioner turns, and receiving evidence-linked evaluation feedback.

Enables training and comparing Logistic Regression and KNN models, making breast-cancer predictions, evaluating confusion-matrix metrics, selecting the best model, generating SHAP explanations, and exporting a Parquet audit log through MCP tools.

Enables querying a FlyWire fruit-fly brain atlas to search, count, describe, and page through 139,248 neurons by cell type, neurotransmitter, superclass, or side using token-efficient selection tickets.

Enables users to train and compare logistic regression and KNN breast-cancer models, get predictions, review confusion-matrix metrics, select the best model, generate SHAP explanations, and export Parquet audit logs through MCP tools.

Enables training and comparing Logistic Regression and KNN models, retrieving sample patient inputs, predicting malignant or benign cases, evaluating metrics, selecting the best model, generating SHAP explanations, and exporting a Parquet audit log through MCP tools.

Enables users to train and compare Logistic Regression and KNN models on the Wisconsin breast cancer dataset, run predictions on patient feature inputs, compute confusion-matrix metrics and select the best model by malignant recall, generate global and local SHAP explanations, and export Parquet audit logs of every tool call. Also provides sample test-set inputs so agents can invoke prediction tools with valid 30-feature payloads.

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

Enables Claude to read a user's Oura Ring data—including sleep, readiness, activity, stress, workouts, heart rate, and personal info—through a self-hosted, read-only connector deployed to the user's own Vercel account.

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.

Search Vascue's public healthcare-operations documentation: AI front desk for clinics, provider-side insurance claims automation, Cliniko integration, security. Public content only.

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.

Verified ICD-10-CM code lookup & validation for AI agents — official descriptions, not guesses.

MCP server for automated ICD-10 medical coding. Code clinical text to ICD-10-CM diagnoses, search 74,000+ codes, and de-identify PHI via the AutoICD API.

MCP server exposing REINVENT4 generative molecular-design operations as validated, LLM-callable tools for drug discovery, including sampling, transfer learning, reinforcement learning, and molecular analysis.

Enables coding agents to interact with the Reactome pathway database, including search, lookup, hierarchy traversal, SBML/SBGN export, and gene-set enrichment analysis.

Provides read-only access to the ProPaths verified protein interactome, letting AI agents search proteins, retrieve mechanistic interaction details, and explore pathway ontology through MCP tools, resources, and prompts.

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.

An MCP server that enables AI coding assistants to interact with Rosetta, PyRosetta, and Biotite for running RosettaScripts, validating XML, translating between Rosetta and Biotite, scoring structures, and querying documentation.

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 AI assistants to search a public therapist catalog by concerns, region, language, and preferences, inspect full therapist profiles, and retrieve crisis resources so users can find an online therapist.