"Pine Script v6 Python integration or comparison" matching MCP servers:

MCP server for NodeBB forums that enables AI assistants to access forum data, search topics, and perform semantic search via the Model Context Protocol.

he Maximo MCP Server bridges AI agents (Claude, Cursor, Antigravity) with your IBM Maximo environment. It empowers your AI to autonomously discover Object Structures (APIs), inspect schemas, and query live data via OSLC. Instead of hallucinating field names, the AI validates queries instantly and ca

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

Implements a Model Control Protocol server integrated with Google Gemini LLM, providing a flexible framework for building AI-powered applications.

A Python MCP server providing AI agents with tools for local knowledge base search, business context retrieval, and text summarization prompt generation.

Generates production-ready short video scripts from real-time web search data using Tavily and Gemini, exposed as MCP tools.

Provides a retrieval/MCP knowledge server for the Unreal Engine Python API, enabling AI agents to retrieve real signatures and documentation before writing code.

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

Production-ready MCP server that integrates OpenAI API with extensible tool support, enabling dynamic plugin loading and knowledge search capabilities through multiple interfaces including CLI and browser UI.

Enables chat with multiple LLM providers (OpenAI and Anthropic) while maintaining persistent conversation memory. Provides extensible tool framework for various operations including echo functionality and conversation storage/retrieval.

A FastMCP-based MCP server for the R2R API, enabling integration with document management, knowledge graphs, and RAG systems through automatically generated tools and resources.

A Python implementation of the Model Context Protocol that enables applications to provide standardized context for LLMs, allowing developers to build servers that expose data and functionality to LLM applications.

Enables MCP clients to connect to LLM/API services using the Model Context Protocol, providing real-time interaction and tool access. Also offers RESTful API endpoints via FastAPI for programmatic integration with existing systems.

A Python port of the official Perplexity MCP server that enables web-grounded Q\&A, deep research, and step-by-step reasoning via Perplexity AI's models. It supports both STDIO and HTTP interfaces for seamless integration with tools like Claude Desktop and Cursor.

A Python-based server providing persistent memory management for AI models with SQLite and Markdown dual backend storage. It features full-text search, RAG-enhanced querying, and cross-project knowledge sharing for integration with Claude, Cursor, and Rovo Dev.

An MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.

Provides file system access for resume matching through JSON-RPC, enabling semantic search and workflow orchestration with LangGraph.

Routes requests to multiple downstream Model Context Protocol servers and provides a LangGraph.js-based agent with RAG capabilities for development assistance.

An MCP-compatible Flask server that integrates with the educhain Python library to dynamically generate educational content for Claude Desktop, including multiple-choice questions, lesson plans, and flashcards.

Orchestrates multiple AI agents to process complex queries by intelligently splitting tasks, executing them in parallel, and synthesizing results using local Ollama LLM inference.