"Creating files with a public endpoint" matching MCP servers:

A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.

A very simple vector store that provides capability to watch a list of directories, and automatically index all the markdown, html and text files in the directory to a vector store to enhance context.

一个基于FastMCP协议的文件向量化服务，支持自动目录监控、多格式文件处理和高效向量搜索，帮助构建个人或团队知识库。

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.

This server enables semantic search capabilities using Qdrant vector database and OpenAI embeddings, allowing users to query collections, list available collections, and view collection information.

A local-first, privacy-preserving centralized memory hub for AI agents — MCP-compatible, zero cloud egress.

Enables hybrid search (BM25 + semantic with RRF) over documentation, with Thai language support. It exposes MCP tools for adding documents and searching via Qdrant and Ollama/OpenAI, using a stateless Streamable HTTP transport.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

Enables storing and retrieving text in a local vector store using Qdrant, running fully locally with no external database or API key.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

askDB is an MCP server that retrieves relevant database schema (DDL) from a Pinecone index and provides it to LLMs to write SQL, without connecting to the database itself.

Enables natural-language search over locally indexed files such as markdown, text, images, videos, and PDFs, and retrieves indexed text or media metadata by path. It lets Cursor query a local embedding index built with Gemini and SQLite.

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Indexes local Markdown/text files into a SQLite database with vector embeddings and provides MCP tools for semantic search without cloud dependencies.

Enables agents to run hybrid dense and BM25 search over a local folder of Markdown files, read and write notes, and trigger reindexing as the folder changes. It also injects the most relevant sections into each prompt automatically and runs entirely locally with a bundled embedding model.

Enables natural-language semantic search of a project's code through a local vector index, so questions that plain grep cannot match return ranked file:line references. It also indexes or refreshes projects on demand, reports index status, and optionally flags near-identical code across files.

An MCP server that provides AI assistants with access to Multi Theft Auto: San Andreas function documentation through vector similarity search and smart keyword expansion. It enables efficient information retrieval with features like deprecation warnings and SQLite caching for technical documentation.