"Detecting Patterns and Conformations in Trading Charts" matching MCP servers:

Persistent memory and pattern learning for AI coding assistants via MCP.

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.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

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.

Enables agents to synthesize typed MCP tool schemas at runtime, validate Python ASTs for unsafe imports, compile sandboxed callable handlers, and hot-plug them into an active MCP runtime. It provides a zero-dependency, standard-library-only MCP server for safe just-in-time tool creation and execution.

Enables agents to dynamically generate typed MCP tool schemas, validate Python execution ASTs against unsafe imports, and hot-plug sandboxed callable handlers into an active runtime. This allows new tools to be synthesized and safely executed on demand.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

A production-ready foundation template for building Model Context Protocol (MCP) servers with FastAPI, featuring modular tools, comprehensive testing, and OpenShift deployment configurations. Includes automated transformation scripts to create custom domain-specific MCP servers.

Wraps Claude Code as a server using the Model Context Protocol (MCP), allowing teams to interact with Claude Code through a Slack bot interface.

Data-visualization MCP server for ROOT Workspace. It enables creating and iterating on charts (Vega-Lite, Python plots) with a persistent kernel and vision loop.

Enables AI models to write, format, compile, lint, and structure modern Fortran code using best practices and industry design patterns.

LLM Driven Trading Platform Orchestration - Strategy Design, Research & Implementation

Enables AI assistants to interact with Interactive Brokers trading accounts to retrieve market data, check positions, and place trades. Includes pre-configured IB Gateway and handles OAuth authentication automatically.

A comprehensive learning project demonstrating MCP server implementation with tools for calculator operations, file management, and system information retrieval, including Google ADK integration patterns and best practices.

MCP server enabling AI agents to programmatically connect to and control Apache Superset instances, allowing users to manage dashboards, charts, databases, datasets, and run SQL queries through natural language interactions.

Enables pattern-based file editing operations using copy/paste functionality with text landmarks instead of exact string matching. Allows AI agents to efficiently manipulate file content by identifying code patterns and insertion points without consuming large amounts of context tokens.

A sample MCP server implementation in TypeScript that demonstrates tool creation with a mock AI completion endpoint. Provides a basic example for developers to learn MCP server development patterns and tool registration.

Enables AI agents to interact with the Roark API by writing and executing TypeScript SDK code in an isolated sandbox, with built-in documentation search to guide those tasks.

A Machine Context Protocol server that enables Claude AI to interact with tools through a structured communication interface, following standard MCP patterns with server initialization and stdio transport.

Model Context Protocol server to run Python code in a sandbox.