Blpapi MCP Server

Blpapi‑MCP: Bloomberg Data for AI Assistants

Blpapi‑MCP is an Model Context Protocol (MCP) server that bridges the gap between AI assistants and Bloomberg’s proprietary financial data feed. By exposing a lightweight HTTP(S) endpoint that streams Bloomberg queries via the blpapi library, it enables Claude, Cursor, Aider, and other MCP‑compatible tools to request real‑time market information without leaving the AI workflow. The server requires a running Bloomberg Terminal (or BBComm) to access data, but once configured it behaves like any other MCP resource: clients can declare the server in their , add it via Claude Code’s command, or integrate it into custom tooling.

Why this matters for developers

Financial professionals and data scientists often need to combine large‑language‑model reasoning with live market feeds. Traditional approaches involve writing custom SDK wrappers, handling authentication tokens, and managing connection pools—all of which can distract from core logic. Blpapi‑MCP abstracts these concerns behind the MCP interface, allowing developers to issue Bloomberg requests as if they were calling a simple REST endpoint. This simplifies the integration of real‑time pricing, reference data, and event streams into AI agents that can interpret, summarize, or act on the information instantly.

Core capabilities

• SSE‑based streaming: The server can push responses incrementally, keeping latency low and enabling incremental parsing by the AI client.
• Full blpapi support: It exposes Bloomberg’s request/response model, including field lists, securities, and reference data.
• MCP‑compatible: Works out of the box with Cursor, Claude Code, and other MCP‑aware tools without custom adapters.
• Secure local deployment: Runs on localhost by default, ensuring that Bloomberg credentials remain on the host machine and are not exposed over the network.

Real‑world use cases

• Automated trading research: An AI assistant can pull the latest price, volume, and corporate action data to evaluate trade ideas on the fly.
• Portfolio monitoring: Developers can build dashboards where an LLM explains portfolio performance in natural language, pulling live data from Bloomberg.
• Risk analytics: Real‑time market data feeds into AI models that calculate VaR, stress tests, or credit exposure, providing instant risk reports.
• Compliance and reporting: AI agents can fetch regulatory filings or event data from Bloomberg, summarizing changes for compliance officers.

Integration workflow

1. Start the MCP server on a machine with Bloomberg Terminal access.
2. Configure your MCP client (Cursor, Claude Code, etc.) to point at the server’s SSE endpoint.
3. Issue Bloomberg requests using the standard MCP tool syntax; the server forwards them to blpapi, receives responses, and streams results back.
4. Leverage AI logic to interpret the data—whether generating natural‑language explanations, triggering alerts, or feeding downstream analytics.

Unique advantages

Blpapi‑MCP eliminates the need to embed Bloomberg SDK logic in every AI project. Its SSE streaming keeps latency minimal, which is critical for time‑sensitive financial decisions. Because it adheres strictly to MCP standards, developers can swap in different data sources or extend the server without changing client code. The result is a clean, reusable bridge that lets AI assistants harness Bloomberg’s rich data ecosystem with minimal friction.