"Understanding OAuth 2.0 Protocol" matching MCP servers:

Enables non-multimodal models to see images by providing MCP tools for image understanding and OCR, backed by any OpenAI-compatible vision model.

Enables AI assistants to autonomously perform site reliability engineering including monitoring, root-cause analysis, impact assessment, and remediation with cryptographic zero-trust enforcement.

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.

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.

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.

Bridges a local AI agent to a Colab session in the browser, enabling code execution and file operations through the Model Context Protocol.

Provides echo tools for testing MCP protocol functionality with message echoing, delayed responses, and JSON data analysis capabilities.

Enables AI agents to interact with Salesforce through MCP, supporting queries, records, metadata, and bulk operations with flexible OAuth authentication.

Provides AI assistants with direct access to the complete Godot Engine documentation, including classes, tutorials, and features. It enables developers to retrieve and analyze official documentation through natural language interfaces using the Model Context Protocol.

MCP server for Overseerr media request management. Enables AI assistants to search for media, view requests, and manage media requests through the Model Context Protocol.

Enhanced fork of Google's Colab MCP, pre-registers all 7 notebook tools at startup (get_cells, add/run/update/delete/move_cell, add_text_cell), adds programmatic GPU control (T4/L4/A100) via OAuth, and fixes the "Disconnected from local server" issue with IPv4-only bind, PNA CORS headers, and stale-tab dedup. Works on Chrome, Edge, Windows, macOS, Linux.

A Python MCP server that enables AI assistants to manage Todoist tasks and projects through the Model Context Protocol. It supports full CRUD operations for tasks and projects, including support for nested projects and Todoist's advanced filter syntax.

A Model Context Protocol server that provides programmatic APIs for running mutation tests with mutmut, analyzing results, and improving test coverage in Python projects.

Enables AI applications to interact with Amazon Advertising API through the Model Context Protocol. Supports campaign management, performance reporting, audience targeting, and comprehensive advertising operations across Amazon's advertising ecosystem.

Provides tools to query, search, and list Google Calendar events using the Model Context Protocol. It enables natural language interaction for checking schedules, searching for keywords, and retrieving upcoming meetings.

A multi-tenant MCP server platform that hosts isolated MCP servers with OAuth/API key authentication for 23+ services, enabling integration with Claude Desktop and other clients via a web interface and CLI.

A test MCP server that exercises all protocol features including tools, resources, prompts, and sampling to help client builders validate implementations.

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.

Automates competitive research for startup ideas by executing a 7-step pipeline including idea understanding, competitor discovery, profile extraction, feature comparison, market gap analysis, innovation scoring, and report generation.