Digital Science launches Dimensions MCP servers
Digital Science has launched two new Model Context Protocol (MCP) servers for its Dimensions platform, enabling enterprise AI agents to access the company’s research intelligence database through the emerging MCP standard.
The company says the new integrations allow AI assistants to connect directly to live, licensed Dimensions data, giving organisations access to more than 430 million interconnected records spanning publications, grants, patents, clinical trials, datasets and policy documents.
The launch comprises two complementary products: Dimensions Semantic Search MCP, designed to improve concept-based retrieval across more than 40 life science domains, and Dimensions Analytics MCP, which provides AI agents with access to Dimensions’ linked research database for analytics and intelligence workflows.
According to Digital Science, existing Dimensions API customers can begin using the MCP servers immediately without requiring an additional licence.
The company says the technology is intended to address a common limitation of enterprise AI systems, which often rely on general-purpose training data that may be outdated, incomplete or difficult to verify. By connecting AI agents directly to structured research data through the MCP standard – which is supported by AI platforms including Claude, ChatGPT and Gemini – organisations can automate research discovery, competitive intelligence and funding analysis within existing AI workflows.
The Semantic Search MCP is designed to retrieve information based on scientific concepts rather than keywords, allowing users to search across publications, patents, grants and clinical trials while identifying relationships between drugs, diseases and compounds.
Meanwhile, the Analytics MCP provides access to Dimensions’ linked dataset, enabling organisations to map research landscapes, profile institutions and investigators, analyse funding trends and connect research outputs with organisations, people and funding sources through a single interface.
Sebastian Schmidt, EVP Enterprise at Digital Science, said: “Research-intensive organisations have invested significantly in AI – the models, the workflows, the infrastructure. What they need is an authoritative bridge between AI and research intelligence.
“Our new Dimensions MCP integrations do exactly that. Whether a team is mapping the competitive landscape, identifying technology transfer opportunities, tracking IP developments, or scanning the horizon for emerging research trends, their AI agents can now draw on live, structured data from Dimensions – one of the world’s largest interconnected global research databases. That’s a meaningful shift for enterprise teams making high-stakes decisions.”
Peter Haase, VP Knowledge Graph Technologies at Digital Science, said: “Dimensions Semantic Search MCP is designed specifically for the complexity of life science terminology. A traditional keyword search for ‘PFAS’ only finds documents containing that exact term. Semantic search, by contrast, identifies the underlying scientific concept and uses domain ontologies to recognise the substances that belong to that concept, such as PFOS, PFOA, PFHxS, and others.
“Rather than relying on users to anticipate every relevant term, abbreviation, or naming variation, the system searches at the level of meaning represented by the ontology. For teams involved in drug discovery, medical affairs, biotechnology, or regulatory intelligence, that difference is significant. It enables researchers to find scientific evidence based on concepts rather than keywords, bringing search closer to the way domain experts think about a subject.”