Digital Science Connects AI Agents to World-Leading Research Data with New Dimensions MCP Servers

Enterprise AI agents can now draw on Dimensions' 430M+ interconnected research records - for research discovery, life science search, and funding intelligence

London, United Kingdom--(Newsfile Corp. - August 10, 2026) - Digital Science, a leading technology company serving stakeholders across the research ecosystem, today announced the launch of new Model Context Protocol (MCP) servers for Dimensions, giving enterprise AI agents direct, license-aligned access to one of the world's largest interconnected global research databases.

The new integrations comprise two purpose-built and complementary MCP servers:

• Dimensions Semantic Search MCP, a concept-aware search layer built for precision retrieval across 40+ life science domains
• Dimensions Analytics MCP, which connects AI agents to 430M+ interconnected records spanning publications, grants, patents, clinical trials, datasets, and policy documents

No other MCP integration in the research space comes close to the breadth of documents, content, and data available from the Dimensions MCP servers. For R&D teams, pharma and biotech organizations, and competitive intelligence professionals, this opens up AI-assisted analytics across one of the world's most comprehensive linked views of global research activity, for discovery and analysis.

Existing Dimensions API customers can connect immediately, with no additional license required.

Research intelligence at the speed of AI workflows

Enterprise R&D teams, pharma and biotech organizations, and competitive intelligence professionals face a persistent challenge: the AI agents they depend on for faster decisions typically draw on general-purpose training data that may be months or years out of date, lacks the depth of peer-reviewed research intelligence, and can't always be verified. These issues are compounded by AI hallucinations. Without access to verified, structured research data, agents may generate responses that sound authoritative but have no reliable source. Manual data aggregation from fragmented sources remains the default workaround, making the work time-intensive, error-prone, and difficult to scale.

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