Finbud Data API Client

Overview

The Finbud Data MCP server bridges the gap between AI assistants and financial market intelligence by exposing a rich, type‑safe interface to Finbud’s REST API. It solves the common developer pain point of having to manually craft HTTP requests, handle authentication, and parse responses for financial data. By packaging the entire API into a single TypeScript library, the server allows AI assistants to retrieve analyst estimates, earnings projections, and other market metrics with minimal boilerplate. This simplifies data ingestion workflows for applications that need up‑to‑date financial insights, such as portfolio management tools, trading bots, or research dashboards.

At its core, the server offers a declarative client that manages authentication via an API key and automatically applies retry logic for transient network or server errors. Developers can specify per‑request options like and , giving fine‑grained control over request resilience. The library’s comprehensive error hierarchy (e.g., , , ) allows AI assistants to gracefully handle failures and implement fallback strategies. Because every method returns a typed promise, static analysis tools can catch misuse early, and IDEs provide inline documentation for each endpoint.

Key capabilities include:

• Full API coverage: Every public endpoint of the Finbud Data service is represented, from analyst estimates to market summaries.
• Type safety: Request parameters and response payloads are defined in TypeScript, eliminating runtime type errors.
• Built‑in resilience: Automatic exponential backoff retries for 408, 409, 429, and server errors reduce the need for manual retry logic.
• Customizability: Timeout and retry settings can be overridden globally or per request, enabling fine‑tuned performance tuning.
• Raw response access: The method exposes underlying HTTP headers, useful for pagination or rate‑limit monitoring.

Typical use cases involve AI assistants that need to pull the latest analyst consensus for a ticker, compare historical estimates against current prices, or generate dynamic reports. For example, a conversational agent could answer questions like “What is the consensus earnings estimate for AAPL this quarter?” by internally calling and formatting the result for the user. In algorithmic trading, an assistant could trigger rebalancing logic when a significant change in consensus is detected.

The server’s integration into AI workflows is straightforward: the assistant sends a request to the MCP endpoint, receives a structured JSON payload, and can immediately feed that data into downstream models or visualizations. Because the library handles all low‑level HTTP concerns, developers can focus on business logic rather than networking intricacies. Its design aligns with modern TypeScript practices, making it a compelling choice for teams that value safety, clarity, and rapid iteration when building AI‑powered financial applications.