Dimensions MCP servers – 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agents they depend on for faster decisions typically draw on general-purpose training data that may be months or years out of date, lacks the depth of peer-reviewed research intelligence, and can’t always be verified.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.
The Dimensions MCPs address this directly. By connecting AI agents to live, structured Dimensions data through the MCP standard – compatible with every major AI platform including Claude, ChatGPT, and Gemini – teams can automate research discovery, funding intelligence, and competitive landscape analysis without leaving the workflows they already use, and without custom integration work.
Used together, the two MCPs give AI agents both the depth to find the precise evidence and the breadth to map the research landscape around it, all through a single Dimensions API connection.
Dimensions Semantic Search MCP
Dimensions Semantic Search MCP gives AI assistants the ability to search scientific literature the way expert scientists do – by meaning, not just keywords. The result is an AI that can answer complex life science questions with real precision and depth, across publications, patents, grants, and clinical trials.
- Surface relevant evidence across drug classes, disease subtypes, and compound families automatically
- Reveal which drugs, diseases, and compounds appear together across millions of documents – accelerating drug-disease mapping, safety reviews, and pipeline surveillance
- Search across publications, patents, clinical trials, and drug labels in a single interface, with precision retrieval at the section level
- Works immediately across all major AI platforms – no specialist setup required
Dimensions Analytics MCP
Dimensions Analytics MCP gives AI assistants access to one of the world’s largest linked research databases, spanning publications, grants, patents, clinical trials, datasets, and more.
- Map competitive research landscapes across any therapeutic area, geography, or technology domain
- Profile research organizations and investigators, aggregating publications, grants, and funding relationships automatically
- Track funding trends and identify top funders, award sizes, and grant activity across any topic or institution
- Link research outputs, people, organizations, and funding sources in a single query – without manually consulting multiple databases
Connecting AI to authoritative research data
Sebastian Schmidt, EVP Enterprise at Digital Science, said: “Research-intensive organizations 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 recognize 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.”
Discover more about our Dimensions MCP integrations: https://www.dimensions.ai/products/all-products/dimensions-mcps/
