"Information about cursors" matching MCP servers:

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.

Reference MCP server for the mcp-longjobs library: durable long-running tasks with progress polling, mid-flight input, and cooperative cancellation, plus chunked file transfers with resume cursors and sha256 verification. Works on every MCP client today — no Tasks-extension support required.

Enables users to control the cursor in Figma through verbal commands using an agentic AI agent, streamlining the design process with a new interaction method.

An MCP server for analyzing images using OpenRouter vision models, offering capabilities like automatic image resizing, model configuration, and handling custom queries about images.

Enables Claude to remember information about users across chats using a persistent local knowledge graph that stores entities, relationships, and observations.

An MCP-based trivia game server where AI creates rounds with two true statements and one false 'twist' about various topics, letting players guess which statement is false.

Enables Claude to remember information about users across conversations by storing and retrieving data as entities, relations, and observations in a persistent local knowledge graph.

Generates AI-powered clarifying questions for user task requests using Claude, helping gather detailed requirements about tech stack, architecture, deployment, and project scope through categorized, contextually relevant questions.

A server that enables natural-language queries about GitHub repositories and returns answers backed by live GitHub data.

Enables an AI assistant to dynamically discover and invoke MCP tools for document search, employee information lookup, and mathematical calculations, with human-in-the-loop approval for sensitive operations.

Enables agents to get fast, calibrated probabilistic answers from Jev (Typesafe AI) to yes/no, scale, or choice questions about provided material, without using a generative model.

Enables LLMs to communicate with Agent-to-Agent (A2A) protocol compliant agents, providing tools for sending messages, managing tasks, and retrieving agent information.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

Provides AI assistants with long-term semantic memory capabilities through local vector-based storage. Enables storing, recalling, and managing information across sessions with complete privacy using ChromaDB, with no data ever leaving your machine.

Enables LLM assistants like Claude to interact with Zaif cryptocurrency exchange through natural language, supporting market information retrieval, chart data, trading, and account management for BTC/JPY, ETH/JPY, and XYM/JPY pairs.

Monitors your Cursor usage and provides a conserve flag to the agent, helping you manage request quota by batching questions, preferring defaults, and cutting unnecessary confirmations.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Enables AI assistants and other MCP clients to launch and steer Cursor Cloud Agents on repositories or as repo-less research tasks, including model selection, mid-run follow-ups, cancellation, result retrieval, and run listing. Also exposes authentication checks and per-agent or per-run token usage and cost reporting.

Lets an AI agent ask real humans a question when the web genuinely has no answer, and get verbatim replies back with a respondent count. Built for the moment an agent is about to say "you should validate this with real users". Remote MCP, plain POST, or a single GET. No signup, no API key.

Enables coding agents to request calibrated probability judgments over typed questions—yes/no, named choices, or ordered scores—about a described situation, returning one probability per option rather than free text. Runs Cloudflare's Clef model locally on Apple Silicon Macs, with images or video frames optionally attached, and answers delivered as a single MCP tool call.