"Information about cursors (general use or specific context)" matching MCP servers:

MCP server exposing four task-shaped tools (resolve, pay, verify, disclose) for General Liquidity, enabling agents to normalize counterparties, submit intents, verify disclosures, and produce signed disclosures.

Private knowledge base with Markdown pages and assets, exposed via MCP for agent access.

An MCP server that provides information about Utkarsh, including bio, skills, work experience, and portfolio projects, accessible via local stdio or remote HTTP with OAuth.

Enables extracting explicitly stated contract metadata, clauses, and obligations from user-supplied contract text without storing or modifying the data.

An MCP server that lets an AI agent use a real terminal the way a person does: type, press keys, click, read the screen, take a screenshot.

The iOS Simulator for coding agents: builds and runs Xcode projects, reads the screen as compact text, taps and types by label, and runs XCTest and XCUITest case by case.

A Model Context Protocol server that enables role-based context management for AI agents, allowing users to establish specific instructions, maintain partitioned memory, and adapt tone for different agent roles in their system.

Enables AI clients to read and write to a user-owned markdown bucket via S3-compatible storage, providing a persistent shared context layer across different AI tools.

An artifact store that lets coding agents and small teams publish, version, and review web artifacts through an MCP server or CLI, with stable URLs, access gates, and comments.

Local-first documentation for AI agents

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

An MCP server that edits files through a real Neovim instance, giving agents access to LSP diagnostics, formatting, code actions, and plugin commands.

Zero-dependency macOS desktop automation for AI agents. Screenshot, mouse, keyboard, clipboard, and window control via MCP. 18 tools, macOS 13+, one command: npx mac-use-mcp.

Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.

MCP server providing deep PostgreSQL context to AI assistants, including schema DDL, index health, foreign key associations, query execution plans, and performance statistics via read-only tools.

Aggregator MCP proxy that collapses N downstream MCP servers into 4 meta-tools with progressive tool discovery, and compresses large tool outputs (HTML→Markdown, JSON summarization) with full-output retrieval via read_more and a per-session token-savings report.

Long AI conversations fail in predictable ways. Context-First fixes all four: Failure Mode What Goes Wrong Context-First Solution Context Drift AI forgets earlier decisions and intent as the conversation grows context_loop + detect_drift continuously re-anchor every turn Silent Contradiction New inputs silently overrule established facts — the AI doesn't notice detect_conflicts compares every inp

Intelligent context manager for AI coding assistants that uses a three-level memory system (core, active, archive) to remember project context across conversations.

A system for defining, composing, and managing task-specific contexts for AI-powered development, enabling consistent, high-quality results.

Deterministic, local-first context optimization for AI coding agents. Automatically compresses raw development session data (messages, tool logs, error stack traces, and git diffs) into a compact, task-specific context window. Operates 100% locally with zero external LLM dependencies, ensuring your source code never leaves your machine.

Provides persistent tool context that survives across Claude Desktop chat sessions, automatically injecting tool-specific rules, syntax preferences, and best practices. Eliminates the need to re-establish context in each new conversation.

A plugin that intercepts large MCP tool outputs, indexes them into a local FTS5 knowledge base, and replaces them with summaries, enabling searchable retrieval to save context window space.

Provides persistent memory, identity, and context for Claude sessions through a memory graph, enabling recall, storage, and relationship management of entities and decisions.

A local MCP server that lets compatible agents use the official, signed Computer Use broker bundled with the OpenAI ChatGPT macOS app.

Local + remote terminal interaction control MCP Server. Lets AI agents control interactive TUI programs the way a human would.

Enables agent-native desktop control by driving mouse, keyboard, and interface elements through accessibility-first semantic operations, with fallback to screen coordinates when accessibility cannot reach a target.

A local MCP server that acts as a policy-based firewall for personal context, letting users control agent access to their data with deny-by-default rules, per-field grants, and an audit trail.

An open-source memory layer that provides persistent project context and architectural history for AI development tools across multiple platforms and sessions. It enables AI assistants to maintain a shared understanding of codebases while integrating directly with services like Notion for documentation management.

Enables AI agents to control a persistent Chrome browser through a local daemon, with Jev performing routine actions and the host agent handling reasoning, text, and verification.