Allows dispatching extracted meeting action items to WeChat Work as collaborative task cards, including notification bot endpoints for task assignment and updates.
Allows analyzing Zoom meeting transcripts to extract action items, commitments, dates, and assignees, and prioritize them using an Eisenhower urgency-importance matrix.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@EnterpriseMeetingActionItemExtractor Analyze this Zoom transcript, extract action items, and make WeChat Work task cards."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Enterprise Multi-Speaker Meeting Action Item Extractor & WeChat Work Task Dispatcher. Analyzes conversational meeting transcripts from Tencent Meeting, Zoom, and Teams, extracts explicit commitments, dates, and assignees, performs Eisenhower urgency-importance matrix prioritization, and formats collaborative task cards.
100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.
Related MCP server: claude-slack-to-notion
"""Example usage for EnterpriseMeetingActionItemExtractor."""
import sys
import json
from client import EnterpriseMeetingActionItemExtractor
sys.stdout.reconfigure(encoding='utf-8')
def main():
print("=== Enterprise WorkBuddy Meeting Action Item Extractor Demo ===")
extractor = EnterpriseMeetingActionItemExtractor()
transcript = [
"David: Good morning everyone, let's review the Q4 cloud infrastructure roadmap.",
"ZhangSan: I will prepare the Tencent Cloud compute reservation forecast by Friday.",
"LiSi: Please ensure the Merkle audit verification connector is deployed today, this is an urgent blocker for finance.",
"WangWu: I'll coordinate the WeChat Work notification bot endpoints before tomorrow EOD."
]
print("\n--- 1. Extracting Structured Tasks from Utterances ---")
items = extractor.extract_action_items(transcript, "Tencent Cloud Infrastructure Sync")
print(f"Discovered {len(items)} action items:")
for item in items:
print(f"[{item['item_id']}] ({item['eisenhower_matrix']}) @{item['assignee']} -> {item['task_description']} (Due: {item['deadline']})")
print("\n--- 2. Generating WeChat Work Collaborative Card ---")
card = extractor.generate_task_card(items, "Tencent Cloud Infrastructure Sync")
print(card["card_markdown"])
if __name__ == "__main__":
main()
Start standard JSON-RPC 2.0 server over stdio:
python mcp_server.pyExecute embedded test harness:
python mcp_server.py --testInspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.
Licensed under the MIT License. Copyright © 2026 GenPark AI.
This server cannot be deployed
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