Overview

The trademcp server is a lightweight Model Context Protocol (MCP) implementation that enables AI assistants to interact seamlessly with financial market data and trading operations. By exposing a standardized set of resources, tools, and prompts over HTTP, it turns any external trading API or data feed into a first‑class “tool” that can be invoked directly from the context of an AI conversation. This eliminates the need for custom integrations or manual data fetching, allowing developers to focus on higher‑level business logic and user experience.

Problem Solved

Modern trading applications require real‑time price feeds, order execution, and portfolio analytics. Traditionally, developers must write bespoke connectors for each broker or data provider, manage authentication flows, and expose those endpoints through REST or GraphQL. When an AI assistant needs to place a trade or fetch market snapshots, it must be hard‑wired to these specific APIs. trademcp abstracts that complexity by presenting a uniform MCP interface: the AI can request a tool named “get‑price” or “place‑order,” and the server handles authentication, rate limiting, and data transformation behind the scenes. This removes friction for developers who want to embed AI into trading workflows without rewriting integration logic.

What the Server Does

At its core, trademcp registers a set of tool endpoints that map directly to common trading actions:

• Market data retrieval – fetch current prices, historical candles, or market depth.
• Order management – place, cancel, or modify orders across supported exchanges.
• Account queries – retrieve balances, positions, and trade history.

Each tool is described in the MCP specification with clear input schemas and expected responses. The server also exposes a prompt template that can be used by the AI to format requests and interpret results, ensuring consistent communication. By running on Bun—a fast JavaScript runtime—trademcp delivers low latency responses, which is critical for high‑frequency or algorithmic trading scenarios.

Key Features & Capabilities

• Unified MCP Interface – all trading operations are accessible through the same protocol, simplifying client code.
• Secure Authentication – supports API key and OAuth flows, automatically refreshing tokens when needed.
• Rate‑Limiting & Throttling – built‑in controls prevent exceeding exchange limits, reducing the risk of bans.
• Extensible Tool Set – developers can add new trading actions by defining additional MCP resources without touching the core logic.
• Real‑time Feedback – responses include timestamps and status codes, allowing AI assistants to provide users with up-to-date trade confirmations.

Use Cases & Real‑World Scenarios

• AI‑Powered Trading Bots – a Claude model can analyze market sentiment, then invoke trademcp tools to execute trades in real time.
• Portfolio Management Assistants – users ask for portfolio performance; the AI queries trademcp for balances and positions, aggregates results, and presents insights.
• Educational Platforms – students interact with a virtual trading environment; the AI guides them while trademcp handles simulated order execution.
• Compliance & Auditing – all actions are logged through the MCP interface, providing a transparent audit trail for regulatory purposes.

Integration with AI Workflows

Developers can embed trademcp into existing MCP‑compatible pipelines by registering the server’s endpoint as a tool provider. The AI model receives tool definitions automatically, enabling it to construct calls like without any additional configuration. Because the server follows the MCP spec, it can coexist with other tool servers—such as finance calculators or news aggregators—within the same assistant session, fostering modular and scalable AI workflows.

Unique Advantages

Unlike generic HTTP APIs, trademcp offers a semantic contract that the AI can understand: each tool’s name, parameters, and return type are explicitly defined. This removes ambiguity in natural language processing and reduces the chance of mis‑execution. Additionally, running on Bun ensures minimal startup time and high throughput, making it suitable for latency‑sensitive trading environments where milliseconds matter. The combination of MCP standardization, robust security, and performance gives developers a powerful foundation for building intelligent, data‑driven trading applications.