Dispatches extracted meeting action items to WeChat Work as collaborative task cards and via WeChat Work notification bot endpoints.
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., "@genpark-enterprise-meeting-action-item-extractor-skill extract action items from this Tencent Meeting transcript"
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: MCP Task Management Server
"""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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