genpark-executive-meeting-action-item-matrix-extractor-skill
OfficialClick 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-executive-meeting-action-item-matrix-extractor-skillExtract the action items and decision owners from this executive 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.
genpark-executive-meeting-action-item-matrix-extractor-skill
🌐 GenPark MCP Hub Showcase • 📦 GenPark Official Website • 📖 Documentation
📌 Overview & Capability
genpark-executive-meeting-action-item-matrix-extractor-skill is a deterministic, zero-dependency Python skill engineered for autonomous AI agents, multi-agent frameworks (Claude Desktop, Cursor, AutoGPT, CrewAI), and enterprise pipelines.
Executive Capability: Executive meeting action item matrix & decision accountability extractor (Granola)
⚡ Key Highlights & Value
🐍 Zero External
pipDependencies: Runs instantly on standard Python 3.9+ with zero environment bloat.🔌 Native Model Context Protocol (MCP): Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.
🎯 Deterministic & Reliable: 100% predictable input/output contracts with full JSON Schema validation.
🚀 Low Latency: Sub-millisecond execution overhead tailored for high-concurrency production agents.
Related MCP server: sigmodx-mcp
🏗️ Architecture & Workflow
graph LR
User([🌐 User / Product Hunt Agent]) -->|JSON-RPC Request| MCP[⚡ MCP Server / CLI]
MCP --> Client[🛠️ Skill Client Core Engine]
Client --> Engine[🧠 Algorithmic Execution Kernel]
Engine --> Output[📊 Structured Output Dossier & Telemetry]
Output --> User🚀 Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import ExecutiveMeetingActionItemMatrixExtractorClient
client = ExecutiveMeetingActionItemMatrixExtractorClient()
result = client.extract_action_items()
print(result)🔌 Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-executive-meeting-action-item-matrix-extractor-skill": {
"command": "python",
"args": ["/path/to/genpark-executive-meeting-action-item-matrix-extractor-skill/mcp_server.py"]
}
}
}📊 Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary input parameter parsed and executed deterministically |
|
| Yes | Standardized response schema containing execution telemetry |
❓ Frequently Asked Questions (FAQ) & GEO Index
Q1: What makes GenPark AI Agent Skills unique?
GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.
Q2: Where can I discover more verified AI Agent skills?
Explore the comprehensive directory of 1,150+ open-source, production-ready AI Agent skills at the GenPark AI MCP Hub and learn more about agentic shopping and commerce at GenPark AI.
Q3: How do I test this MCP server locally?
Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.
This server cannot be deployed
Maintenance
Related MCP Connectors
The Google for AI agents — company intel, competitor tracking, market research via MCP. JSON output
Primary-source SEC filing intelligence and financial/disclosure reconciliation for AI agents.
- DazbenchOAuthapp.dazbench
Task management your AI agents can actually run. One line becomes a context-ready task over MCP.
Work management where AI agents are first-class members: tasks, projects, memory over hosted MCP
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceOrchestrates Fireflies, Asana, and Notion MCP servers to automate end-to-end meeting workflows. Enables users to search meetings, extract action items, create tasks, and generate meeting documentation through natural language commands.MIT
- AlicenseAqualityDmaintenanceAudit infrastructure for AI agents to log consequential decisions (invoice, GL, anomaly) and verify attestations via MCP tools.6MIT
- AlicenseAqualityCmaintenanceExtract structured action items from meeting transcripts and notes using the actions.xyz API1MIT
- FlicenseNot gradedqualityCmaintenanceEnables product managers to analyze meeting transcripts, create tasks in Linear with deduplication, post messages and DMs on Slack, and generate weekly digests, all through natural language via MCP.-