"How to interact with Notion" matching MCP servers:

Enables AI agents and RAG pipelines to retrieve live Notion content alongside local vector and BM25 indices, fusing results with Reciprocal Rank Fusion to provide fresh, provenance-aware context.

A Model Context Protocol server that provides a standardized interface for interacting with Notion's API, enabling users to list databases, create pages, and search across their Notion workspace.

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 AI agents to browse, search, and read a curated library of 198 UI/UX and animation design skills across 17 categories, distilled from 10 open-source projects, with a progressive 5-step workflow for guidance.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

An MCP server that retrieves resume/experience evidence relevant to a job description via vector RAG, and tracks fit-analysis results in a configurable tracking store (Notion or SQLite), with tools like match_job, push_to_tracker, and list_applications.

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.

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 AI agents to interact with Amazon Redshift databases using natural language to execute queries, explore schemas, and retrieve sample data.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

MCP Ollama server integrates Ollama models with MCP clients, allowing users to list models, get detailed information, and interact with them through questions.

Enables AI agents to interact with Amazon Redshift databases using natural language to execute queries, list tables, describe schemas, and retrieve sample data.

Enables AI agents to interact with the AI-Archive platform for research paper discovery through semantic search, paper submission and management, peer review with structured scoring, and citation generation in multiple formats.

Connects to Elasticsearch databases using the Model Context Protocol, allowing users to query and interact with their Elasticsearch indices through natural language conversations.

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.

An integration that allows Large Language Models to interact with Raindrop.io bookmarks through the Model Context Protocol, enabling users to create and search bookmarks directly through AI assistants.

Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.

A Model Context Protocol server that enhances LLM capabilities by connecting to Wikipedia, internet search (Tavily), and financial data (Yahoo Finance) tools, enabling contextual responses to user queries.

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 AI assistants to interact with Meilisearch via the Model Context Protocol, allowing comprehensive index, document, and search management through a standardized interface.

Enables AI assistants to interact with Meilisearch through a standardized interface, supporting index and document management, search capabilities, settings configuration, task monitoring, and experimental vector search.

Enables Claude Desktop to trigger and interact with Make.com automation scenarios through webhooks. Allows users to execute complex workflows and integrations with third-party services like Google Sheets, Notion, and Slack using natural language commands.

An optimized read-only server for AI assistants to efficiently query and retrieve Notion content, featuring parallel processing and a minimized toolset of 7 essential read-only operations.

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

Aggregates multiple third-party APIs into unified MCP tools, providing out-of-the-box access to 10 popular services including OpenWeather, Google Maps, GitHub, Notion, Spotify, and more. Enables users to interact with weather data, search places, manage repositories, create content, and access various web services through a single MCP server.

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

An MCP server to interact with a Tinybird Workspace from any MCP client.

Provides LLM access to Exasol database via MCP tools, including metadata reading and data queries, with an optional on-premise text-to-SQL (Governed SQL) translation.

Enables AI assistants and developer tools to securely access and interact with an organization's enterprise knowledge, documents, and people through natural language while respecting existing access permissions.

Enables LLMs to interact with Rememberizer Vector Store for semantic search, document creation, deletion, and modification.