mac-wechat-mcp
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Alternatives to mac-wechat-mcp
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- FlicenseNot gradedqualityCmaintenanceEnables macOS WeChat desktop clients to read conversations via OCR, retrieve encrypted session memory, and perform controlled message sending through MCP with dry-run and supervised safeguards.-
- AlicenseNot gradedqualityCmaintenanceEnables automation of WeChat on macOS through the Accessibility API, allowing LLMs to fetch recent messages from contacts and send replies based on conversation history.49 PyPI255MIT
- FlicenseNot gradedqualityBmaintenanceProvides AI clients read-only access to WeChat chat history by extracting and decrypting the local Mac database, enabling search, summary, and analysis of messages.-
- AlicenseNot gradedqualityDmaintenanceA production-oriented MCP server that exposes task-level WeChat Desktop automation to AI clients, enabling status checks, message fetching/reply, contact addition, and Moment publishing on macOS via accessibility automation.2MIT
- AlicenseNot gradedqualityCmaintenanceAn MCP server that lets an LLM read and drive the macOS WeChat client through the Accessibility API, without reverse-engineering or injected code. It supports listing chats, opening conversations, reading messages, sending actions, and saving media locally.MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to read local WeChat chat records, sessions, contacts, and group members through WeFlow's local HTTP API, keeping all data on localhost.18 npmMIT
TDQS
Scored across 7 tools
Each tool maps cleanly to a distinct stage in the observation workflow: status, start, poll, acknowledge, preview, pause, and demo advance. The descriptions carefully separate internal bookkeeping from WeChat-facing actions, so there is little chance of selecting the wrong tool.
Six of seven tools follow a consistent verb_noun pattern such as get_status, start_observation, poll_changes, acknowledge_observation, preview_reply, and pause_observation. demo_advance is a minor naming outlier, reversing the expected noun-verb order.
Seven tools is well-scoped for the intended observation-and-preview workflow. Each tool adds a distinct capability, and demo_advance is clearly labeled as demo-only rather than bloating the surface area.
The toolset covers the full lifecycle from baseline capture through polling, acknowledgment, reply preview, and pause/restart. The absence of a send action is intentional and consistently documented, so the surface feels complete for its stated no-send, human-in-the-loop purpose.