Solution pairs AI-ready metadata and semantic context with workflow-focused Skills, giving clients' AI agents what they need to interpret Bloomberg data correctly and use it across financial workflows.
NEW YORK and LONDON, Sept. 29, 2026 /PRNewswire/ -- Bloomberg today announced the launch of Bloomberg Enterprise Model Context Protocol (MCP), an AI access layer for Data License Plus (DL+), Bloomberg's next-generation Data License offering. Built for the agentic AI era, the solution enables clients' AI agents to discover, understand, and retrieve licensed Bloomberg data across more than 100 million securities and over 50,000 fields through a standardized MCP interface, helping firms move quickly and easily from AI experimentation to live production workflows.
Most tools can retrieve data points. Fewer can tell an agent what that data point means, how it was calculated, or whether it's the right one for the task at hand. Bloomberg Enterprise MCP is built to close that gap, combining semantic context, AI-ready metadata, and workflow-focused Skills that will give agents the information they need to interpret results correctly and act on them with confidence, across research, portfolio, risk, and operations.
In practice, this changes how fast investors in the global capital markets can get from a question to an answer. Instead of first hunting for the right dataset and then reconciling identifiers by hand, or filing a request and waiting, users can simply ask for what they need in plain language and get back data that already carries the context to trust it: what currency it's in, when it was priced, what it connects to. This shift, from time-consuming manual lookup to an answer you can act on immediately, is what moves generative AI from a pilot into something a firm can run in production every day.
"As generative AI adoption in financial services has grown, the bottleneck facing financial institutions has shifted from model capability to data readiness," said Tony McManus, Global Head of Enterprise Data and Indices at Bloomberg. "We've seen agents that reason well but still can't answer a basic question about a position, because the data reaching them carries no indication of what it actually represents. On its own, a last price doesn't say what currency it's in or what kind of price it is. Connections were never the hard part. Readiness is, and that's what we built Enterprise MCP to solve."
Key Features of Bloomberg Enterprise MCP
- AI-ready metadata and context: Bloomberg's metadata describes what each field means, how it was calculated and when it applies, along with the qualifiers needed to make a number interpretable, such as the exchange, currency, price type, period, as-of date. A standalone number is meaningless to an agent; the metadata is what tells it what the value actually is and when it can be relied on.
- Semantic interfaces over that metadata: That same metadata is exposed via semantic search tools, so agents find the right field by describing what they need in natural language, instead of guessing mnemonics. Entity resolution does the same on the security side, mapping a name, ticker or description to the right instrument and its relationships to the issuing company — so agents don't hard-code identifier lists. Metadata makes the data understandable; the semantic interface makes it findable.
- Breadth of content. Access Data License content through one interface: pricing and reference data across asset classes, company fundamentals and structure, economics and alternative data spanning across DL+ Live and the Qube Datastore (QDS). New content arrives as new capabilities for already connected agents, not as a new integration.
- Task-specific tool design: A single security discovery tool and a single field discovery tool cover the content set, rather than separate tools per asset class.
- Workflow Skills: Reusable procedures covering point-in-time universe retrieval, corporate action adjustment and revision history, which clients will be able to adopt and extend. The initial release will include Skills to identify outliers in a set of tickers, review trades that breach accepted price deviation thresholds and assess securities and trades for potential sanctions exposure, with additional workflow-focused Skills to come.
- Entitlements and audit: Bloomberg validates a firm's entitlements before returning data on any agent request, under standard Data License rights. Requests resolve against the Operational Datastore (ODS), and tool-level limits cap the securities and fields returned in a single call.
- Client-controlled execution: Bloomberg hosts and manages Enterprise MCP, while clients control the AI agents and applications that connect to it, including the models, prompts, instructions and other steering context used in their workflows.
What's Next
Looking ahead, Bloomberg plans to further extend Enterprise MCP with real-time data, supporting the agentic workflows that increasingly depend on price freshness, from intraday monitoring and pre-trade checks to risk refreshes and exception handling. Bloomberg is also delivering a forthcoming solution for Terminal subscribers, to provide the same governed and attributable access to Bloomberg data found in ASKB, the Terminal's conversational AI interface, directly into financial professionals' daily desktop workflows.
Bloomberg Enterprise MCP is an enterprise offering. Clients can license the specific content domains they need under standard Bloomberg Data License rights. Data License clients can browse and access content at https://data.bloomberg.com.
About MCP at Bloomberg
Bloomberg is both building on and helping shape MCP. Bloomberg engineers are the primary authors of proposals that introduce enterprise governance controls into the protocol and allow servers to present tailored toolsets to different clients. Bloomberg started and leads the MCP Financial Services Interest Group and holds seats on both the board and Technical Committee of the Agentic AI Foundation, which stewards MCP as an open standard.
About AI at Bloomberg
Since 2009, Bloomberg has been building and using artificial intelligence (AI) in the finance domain – including machine learning (ML), natural language processing (NLP), information retrieval (IR), time-series analysis, generative models, and agentic AI architectures – to help process and organize the ever-increasing volume of structured and unstructured financial information. With this technology, Bloomberg is developing new ways for financial professionals and business leaders to derive valuable intelligence and actionable insights from high-quality financial information and make more informed business decisions.
About Bloomberg
Bloomberg is a global leader in business and financial information, delivering trusted data, news, and insights that bring transparency, efficiency, and fairness to markets. The company helps connect influential communities across the global financial ecosystem via reliable technology solutions that enable our customers to make more informed decisions and foster better collaboration. For more information, visit Bloomberg.com/company or request a demo.
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SOURCE Bloomberg
