HHS · no agency ID · record R0795

PubMed Literature Review MCP Server

Department of Health and Human Services · HHS/CMS/OIT

Overview

Development stage: PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2025-08 (month precision)Source: Aug-25
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Information Technology
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The PubMed Literature Review MCP server solves the problem that CMS teams need to carry out various types of literature reviews, commonly using PubMed data, but lack an internal tool for doing this at greater scale than manual effort allows

Expected benefits

Enable CMS teams to conduct literature reviews at scale through an internal tool that automates PubMed data analysis and synthesis

System outputs

Literature reviews and research syntheses that combine PubMed data with other contextual information

Technology

AI classification: Agentic AISource: Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.
System name(s): CEDAR
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Contract and in-houseSource: c) Developed with both contracting and in-house resources
Vendor (as reported): Skyward IT Solutions
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: k) None of the above
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported