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

Enables AI agents to search and fetch first-person success stories from AI coding sessions, providing transferable patterns and lessons to improve performance on similar tasks.

MCP server that answers market and wealth-management questions using only HSBC Private Banking insights published at privatebanking.hsbc.com, via OpenAI web search restricted to that domain. It exposes a single tool, lionbank_wealth_insight, for use in ChatGPT Developer mode.

Enables multi-agent financial research through MCP, combining live or offline market, news, fundamentals, document RAG, and risk tools with critic review, synthesis, and execution tracing.

Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.

Exposes portfolio allocation, concentration risk, retirement projections, RAG document search, and a full multi-agent financial advisor query as MCP tools for use from Claude Code.

Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.

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.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

A guide for implementing Model Context Protocol (MCP) servers that provide AI models with external tools like web search, text manipulation, and mathematical operations.

A semantic-memory MCP server that stores text 'memories' with provenance and enables recall by meaning (vector search), keyword (FTS5), or structured filters.

Provides tools for RAG-based financial document retrieval, financial calculations, Yahoo Finance stock data, and portfolio analysis via JSON-RPC 2.0 protocol.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

A Model Context Protocol server that enables AI assistants like Claude to programmatically access financial data from Financial Modeling Prep API, including company profiles, financial statements, metrics, SEC filings, and market data.

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.