"A guide to building wealth and financial success" matching MCP servers:

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Provides financial data from Financial Modeling Prep for AI-assisted investment research, including company profiles, financial statements, and analyst ratings. It features high-level workflow tools for market analysis and atomic tools for deep dives into valuation and institutional ownership.

A starter template for building calculator-focused MCP servers using NitroStack, including tools, resources, and prompts.

Enables AI-driven financial analysis including portfolio analysis, risk assessment, investment simulation, market intelligence, and report generation via NitroStudio.

Provides comprehensive data for A-shares, Hong Kong, and US stocks alongside cryptocurrency markets, supporting technical indicators, news, and financial statements. It features automatic failover across multiple data sources to ensure reliable access to real-time and historical market information.

A Model Context Protocol server framework featuring dynamic tool loading and automatic tool discovery from a dedicated directory. It leverages FastMCP to provide a robust environment for building, configuring, and testing individual Python-based tools.

A verified, event-ready MCP server template for building TypeScript servers with the NitroStack SDK, including a calculator module as a working reference.

A starter template for building a calculator-focused MCP server with tools, resources, and prompts using TypeScript and Zod validation.

A template and demonstration project for building, testing, and deploying remote MCP servers using FastMCP and uv. It provides a foundational structure for creating MCP-compliant tools that can be hosted publicly and integrated with LLM agents.

Enables financial data retrieval, company context, market information, financial modelling, and valuation through MCP tools, with a deterministic sample provider for testing before connecting live data sources.

Enables AI agents to control GNS3 network emulation labs. Supports building topologies, managing devices, capturing packets, and automating device CLIs.

MCP server for integrating with the Organizze financial manager, enabling AI clients to create transactions, check balances, manage accounts, and more via the Organizze API.

Read-only MCP server for the Bucharest Stock Exchange, exposing instrument data, OHLCV candles, and fundamentals through a public backend with no authentication.

A secure MCP server that stores and retrieves API keys and credentials, featuring encrypted storage, masked retrieval, and AI-powered recommendation via MCP sampling.

Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.

Enables AI assistants to query, analyze, and discover FileMaker database schemas via OData v4, with pandas-powered analytics for efficient data summaries.

Connects AI agents to MAVLink drones (PX4, ArduPilot) via a capability layer with safety guardrails, enabling telemetry, flight control, and mission management through natural language.

MCP server that enables cloud models (like Gemini/Claude) to delegate coding tasks to a local llama.cpp server, preserving cloud usage limits through an AI-powered code review loop.

Exposes a Kubernetes cluster to MCP-compatible AI clients, enabling read-only and optional write operations on cluster resources like pods, deployments, and namespaces through natural language.

A Model Context Protocol server built with FastMCP that provides tools for retrieving the current local date and time. It allows LLMs to access precise temporal information directly from the host environment.