"Answer" matching MCP servers:

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.

An MCP server implementing the ROBINS-I V2 framework for risk-of-bias assessment in non-randomized studies, with deterministic algorithms and full provenance. It enables users to parse study documents, specify target trial results, answer signalling questions with evidence-bound quotes, and compute or override domain judgements.

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.

Condition-aware ingredient & product safety intelligence for AI agents. Every answer carries a claim-level evidence attestation, verdict, an evidence tier, and a citation – curated against authoritative sources (LactMed, InfantRisk, PubMed, DSLD, DermNet, EU CosIng) by Health AI. Hosted MCP server – no install, no key. Endpoint: https://mcp.healthai.com (Streamable HTTP, JSON-RPC 2

Enables AI assistants to answer healthcare REIT portfolio questions with deterministic, provable answers grounded in Omega Healthcare 10-K filings and CMS nursing home data, including status, caveats, and provenance.

Enables AI assistants to answer peptide questions from PepBank study summaries with cited sources, and to look up catalog products with variants, prices, stock and per-lot lab reports (COAs). Also covers store policies and generates cart links for checkout on curo.you.

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

Gives AI agents read-only access to patient context on any FHIR R4 server, including journey timelines of encounters and appointments, open booking slots, and discharged patients whose follow-ups were never booked. Search, appointment, and follow-up tools return compact summaries, so agents can answer front-desk and outreach questions without write access.

Enables AI assistants to answer questions about the department's imaging facility—microscopes, lab VMs, protocols, and measurements—by reading a locally synced, read-only copy of the site's library and citing relevant pages.

Enables an AI to generate labeled CT images with answer keys for medical-school practice questions, including click-the-structure hotspot masks that align with the images.

Enables agents to search six clinical-trial channels (global registries, Chinese registries, CDE drug-trial data, and offline snapshots) through one sealed endpoint, merging results only on confirmed registration identity and returning per-source terminal states, coverage caveats, and data cutoffs. This ensures a missing or empty answer can never be silently mistaken for proof that no trial exists.

Enables Claude, Cursor, or any MCP client to query public Drosophila connectome datasets and run in-silico experiments on them locally, with every answer carrying built-in controls, uncertainty, and provenance. Currently exposes dataset listing with licence, citation, download size, and locally cached level details.

Enables testing healthcare AI agents against cited FDA labels and coded answer keys by verifying medication order text and structured claims deterministically, with cited results, FDA reference lookups, and scored exams.

Enables AI agents to run SQL analytics against healthcare claims through a de-identified mart, with privacy enforced inside the database engine — small-cell suppression, read-only authorization, resource budgets, and a tamper-evident audit log — so agents can answer questions about denial rates and costs without ever touching PHI.