mcp-server-macos-use
> MCPserver for macOS desktop automation using native accessibility APIs
## Overview
mcp-server-macos-use is a Model Context Protocol (MCP) server written in Swift that
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what the person is signed in to — driven from a CLI and an MCPserver.
## Docs
- [README](https://raw.githubusercontent.com/SirAllap/agentglass/main/README.md): what it does, screen by screen, platforms, install, FAQ, roadmap
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resources for AI agents, including docs indexes, MCP access, skills, and recommended starting points
- [MCPServer](https://docs.base.org/get-started/docs-mcp): Connect your AI coding assistant to Base documentation using Model Context
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published. The ovs CLI is the canonical interface, mirrored 1:1 by an MCPserver, and can be picked up via native skills, MCP tools, or a self-describing
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real time inside one working session. It relays messages between an MCP channel and the Codex app-server protocol so the two agents can cross-review each other's diffs
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cloud account and no spending.
- [MCP tools for AI agents: a self-hosted client](https://kwant.si/docs/mcp): Connect Model Context Protocol servers to your kwant bots, with per-bot access
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sources. No API key required.
> REST JSON, RSS/Atom, WebSocket, AI analysis, historical archive, MCPserver, ChatGPT plugin.
> Open source (MIT). https://cryptocurrency.cv
This document is the complete inline reference intended
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required. REST JSON, RSS/Atom feeds, WebSocket streaming, AI analysis, historical archive, MCPserver, ChatGPT plugin. Source-available (all rights reserved); the hosted API is free to use.
Free Crypto News
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sources. No API key required.
> REST JSON, RSS/Atom, WebSocket, AI analysis, historical archive, MCPserver, ChatGPT plugin.
> Source-available, all rights reserved; the hosted API is free
33335d agoReviewMountCreate a free account to mount
USDC micropayments via x402. REST JSON, RSS/Atom feeds, WebSocket/SSE streaming, AI analysis, historical archive, MCPserver, ChatGPT plugin.
> Source-available, all rights reserved; the hosted API is free
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GraphQL API to AI agents through MCP.
```python
from pydantic_resolve import AppConfig, create_mcp_servermcp = create_mcp_server(
apps=[
AppConfig(
name="blog",
er_diagram=diagram,
description="Blog
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/rife2/bld/wiki/Custom-Commands): add a project command by writing a method and annotating it with @BuildCommand
- [MCPserver](https://github.com/rife2/bld/wiki/Standard-Commands#mcp): serve every build command as an MCP tool over stdio, register
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drawn on the picture or mixed into the sound.
- Every result from the MCPserver and from the command line with --json includes an `attribution` object. Its `credit_line
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drawn on the picture or mixed into the sound.
- Every result from the MCPserver and from the command line with --json includes an `attribution` object. Its `credit_line
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