"Using mcp-proxy to bridge a remote SSE server to a stdio server" 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.

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.

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 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.

Bridges STDIO-based MCP clients with SSE-based MCP servers, allowing applications like Claude Desktop to connect to remote MCP servers that use SSE transport.

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

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

Automatically converts OpenAPI/Swagger documents into MCP servers, enabling AI agents to call third-party REST APIs via the MCP protocol.

Enables an agent to run ten ready-made research and monitoring workflows — competitor digests, cited reports, paper alerts, market maps, regulatory and due-diligence watches — pulling from Firecrawl, Tavily, GitHub and landing results in Notion, Slack, Google Docs, Sheets and Linear. Fields are filed with the user's own keys held in the operating system keychain, and every action that creates, sends or changes anything is shown and approved first.

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

A server for Model Context Protocol (MCP) that uses Server-Sent Events (SSE) for streaming communication, enabling tools like the HackerNews API to be accessed through a secure HTTP+SSE transport.

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.

MCP server enabling natural-language querying of SQLite databases via schema discovery, GraphRAG retrieval, and safely guarded read-only SQL execution.

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

reference MCP server over SSE transport — calculator domain, teaching patterns

Translates plain English questions about infrastructure operations into SQL queries, executes them against a database, and returns the real answer.

Provides fast static embedding, similarity, and search capabilities via MCP tools and an OpenAI-compatible HTTP API using a tiny 16M-parameter English embedding model.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

A Cloudflare Worker that proxies the Tavily API through MCP, offering search, extract, crawl, and map tools with automatic API key rotation and health-based routing.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

A type-safe solution that enables remote access to Model Context Protocol (MCP) services, allowing clients to connect to centralized MCP implementations without waiting for official remote support.

A comprehensive MCP proxy server that bridges MCP clients with Ollama local language models, providing advanced features like RAG integration, context management, caching, and production-ready security.

Enables clients of tool-heavy MCP aggregators to find and use upstream tools on demand through four fixed bridge tools (search, describe, call, refresh), cutting per-turn tool context from ~180k tokens to ~2–3k. It merges multiple upstream servers behind sticky sessions and a local lexical BM25 index, so only the schemas actually needed are retrieved.

A flexible proxy that enables discovery and execution of tools across multiple Model Context Protocol (MCP) servers and JavaScript functions, reducing context size even when dealing with hundreds of tools.

A configurable MCP server that dynamically loads capabilities from a remote configuration to bridge MCP clients with remote APIs for executing actions, accessing resources, and utilizing prompt templates.

Exposes any SQLite database as read-only MCP tools for AI assistants, enabling listing tables, describing schemas, and running SELECT queries with filtering, ordering, and pagination.

A semantic proxy that reduces AI agent token usage by exposing only three core tools and using local vector embeddings to search for and execute hundreds of underlying MCP tools. It streamlines communication between agents and MCP Routers by identifying relevant tools through natural language queries.