Securities Prices MCP Server

Overview

The Mcp Investments Sample is a ready‑to‑run MCP server that exposes financial market data as AI‑friendly tools. By publishing securities information—current prices, historical trends, sector performance, and more—the server turns raw market data into a conversational resource that Claude or any MCP‑compatible assistant can query on demand. This eliminates the need for developers to build custom connectors or maintain separate data pipelines when they want their assistants to answer finance‑related questions.

What the server does is straightforward yet powerful: it loads static JSON files that contain market snapshots and historical records, then exposes four distinct tools. These tools let an assistant retrieve a single ticker’s real‑time snapshot, pull all securities in a sector, fetch historical price and volume data for any date range, or compare a sector’s performance against its constituents. Because the tools are defined in MCP terms—each with clear input schemas and output structures—the assistant can automatically generate prompts, validate arguments, and present results in a consistent format. This integration streamlines development by removing boilerplate code for data validation and error handling.

Key features include:

• Seamless tool discovery – The server registers all four tools in its MCP manifest, making them instantly available to any client that queries the endpoint.
• Rich financial context – Each tool returns structured data (price, volume, sector metrics) that can be directly used in downstream calculations or visualizations.
• Date‑range querying – Historical data can be filtered by start and end dates, enabling trend analysis or back‑testing scenarios.
• Sector analytics – By aggregating performance metrics across a sector, developers can build comparative dashboards or advisory queries.

Typical use cases span financial analysts, fintech startups, and educational platforms. A portfolio manager might ask the assistant to “compare Apple’s current price with its sector average,” while a data science student could request historical trends for multiple tickers to feed into a machine learning model. Because the server is built on the C# MCP SDK, developers can easily extend it with live data feeds or additional financial instruments without changing the core MCP interface.

Integration into AI workflows is effortless. Once the server is running, any MCP client—such as Claude for Desktop or a custom chatbot—can load the tool definitions and invoke them with natural language prompts. The assistant handles argument extraction, calls the appropriate MCP endpoint, and formats the response back to the user. This tight coupling between data and conversational AI removes friction from building finance‑aware assistants, allowing developers to focus on higher‑level business logic rather than plumbing.