"Executing Orders on Alpaca Trading Platform" matching MCP servers:

Alpaca’s official MCP Server lets you trade stocks, ETFs, crypto, and options, run data analysis, and build strategies in plain English directly from your favorite LLM tools and IDEs

An MCP server that enables AI models like Claude to interact with the Trading Simulator API for checking balances, viewing prices, and executing trades with automatic chain detection.

The Problem Modern distributed systems and AI agents require robust operational scaffolding. Simple CRUD apps or mock loops fail when subjected to real-world edge cases, asynchronous boundaries, and security constraints. The Solution An isolated execution platform for AI agents. An agent is given a broken service and a bounded toolset inside a disposable sandbox. Every command passes throug

MCP server for the Agent Platform API, enabling management of sandbox environments through MCP protocol. Exposes tools to interact with sandbox lifecycle operations such as start, stop, and status checks.

A Node.js-based trading bot that integrates with the Zerodha platform to provide automated stock trading operations through MCP tools including session management, buy/sell operations, and portfolio tracking.

An MCP Server that enables users to interact with Google's On-Demand Scanning API (v1beta1) through natural language, likely providing capabilities for vulnerability scanning and security analysis of containers and artifacts.

Enables GUI automation by executing Python code with instrumented pyautogui, providing MCP-friendly output and inline screenshot delivery.

A modular platform that enables LLM agents to discover, register, and execute both local tools and tools from external MCP servers, with REST APIs for server management and Streamable HTTP support.

A deployable MCP server for Cloudflare Workers or Azure Functions that provides example tools (time, echo, math), prompt templates for code assistance, and configuration resources. Enables AI assistants to interact with edge-deployed services through the Model Context Protocol.

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

AI-driven tool that helps developers create beautiful UI components instantly through natural language descriptions, integrating with popular IDEs like Cursor, Windsurf, and VSCode.

Turns any LLM into a coding co-pilot with production-ready MCP server providing lint, test, execution, generation, and graph tools. Features Neo4j persistence, OpenAI Agent integration, REST API, and React frontend for comprehensive code development assistance.

A platform that transforms AI development with intelligent context management, optimization, and prompt engineering, enabling developers to enhance model performance through structured context management and optimization tools.

A server implementation for the Model Context Protocol (MCP) that runs on Cloudflare Workers and supports OAuth login, allowing Claude AI to use custom tools through local or remote connections.

Enables deployment of MCP servers on Cloudflare Workers with OAuth authentication and remote connectivity. Provides a template for creating cloud-hosted MCP servers that can be accessed by Claude Desktop and other MCP clients over HTTP/SSE.

A serverless deployment for Model Context Protocol (MCP) on Cloudflare Workers that enables AI agents to use custom tools without authentication requirements.

A tool that allows deployment of a remote MCP (Model Context Protocol) server on Cloudflare Workers without authentication requirements, enabling connection to AI models through Claude Desktop or Cloudflare AI Playground.

Facilitates interaction with AWS Bedrock-enabled tools by implementing Anthropic's Model Context Protocol for seamless tool integration and communication.

Enables AI assistants to interact with the Zerodha trading platform for placing stock orders, viewing portfolio holdings, and managing mutual fund investments. Provides secure OAuth authentication and real-time trading capabilities through Zerodha's official API.

Enables AI agents to interact with Jupyter notebooks via MCP tools for querying, modifying, executing, and setting up notebooks, with state preservation and real-time collaboration.

Connects MCP clients to a local Blender + Bonsai (BlenderBIM) session for scene inspection, IFC project querying, viewport screenshots, and executing Python code in Blender.

Enables analysis of bulk RNA-seq data using natural language queries, executing R and Python in a Docker container with automatic sample anonymization and privacy controls.

Enables executing submitted code snippets in a fresh isolated V8 isolate, capturing stdout, stderr, return values, and failures under enforced resource limits.

MCP server to give every agent an ephemeral Linux sandboxes for executing shell commands.

Enables AI coding agents to compile Delphi projects programmatically by parsing .dproj files, executing the Delphi compiler, and returning structured error results with multi-language support and automatic configuration generation from IDE build logs.

Speeds up coding agents by predicting and pre-executing upcoming tool calls, then safely returning validated cached results to eliminate wait time.

Enables Claude Code to perform programmatic tool calling by executing Python scripts that interact with multiple MCP servers in a single round-trip. This reduces latency and token consumption by keeping intermediate tool results within the local Python runtime instead of the conversation context.

Lets an AI agent read and change on-premises Active Directory by writing short JavaScript scripts that run in a network-isolated sandbox against a chosen domain. Runs on Windows as the logged-on user, over Kerberos, with tools for executing scripts, searching the domain's schema catalogue, querying Microsoft Learn docs, and managing domain connections and Group Policy objects.

Enables AI assistants to write and debug integration tests by providing live access to DOM structure and executing code directly in test environments.

Enables MCP clients to manage disposable cloud Linux machines by creating and controlling them, executing commands, reading and writing files, creating snapshots, publishing ports, and issuing SSH tokens.