Analyze stocks with summaries, price targets, and analyst recommendations. Track SEC filings, divi…
Quick Install
Automated & IDE SetupCopy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
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Manual Client & Custom JSON ConfigExpand JSON ▾
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Documentation Overview
AI Agent Tutorials & Implementations
A comprehensive collection of production-ready AI agent implementations showcasing different frameworks, protocols, and integration patterns. This repository demonstrates various approaches to building intelligent agents with Model Context Protocol (MCP), multi-agent systems, and real-world integrations.
Repository Overview
This repository contains multiple agent implementations, each demonstrating different architectural patterns and use cases:
| Project | Framework | Key Features | Use Case |
|---|---|---|---|
| agent2agent | LangGraph + A2A Protocol | Remote agent communication, Slack integration | Investment research |
| mcp-financial | FastMCP + FastAPI | ASGI integration, CLI client | Financial data analysis |
| bright-mcp-server-overview | Dual: LangGraph + ADK | Memory persistence, extended timeouts | Web scraping & research |
| fpl-deepagent | FastMCP + React UI | Streamable HTTP, ChatGPT integration | Fantasy Premier League |
| task-manager-app | FastMCP + React UI + Supabase | OAuth (Auth0), per-user DB state, Slack notifications | Task management in ChatGPT |
| notion-mcp-agent | LangGraph + MCP | Notion integration, database management | Knowledge management |
| claude-advanced-tool-use | Claude API + FastMCP | PTC, Tool Search, MCP integration | Token-efficient AI agents |
| claude-skills | Claude Skills API | Document generation, custom skills | PowerPoint, Excel, Word creation |
| openai-chatkit-starter-app | Next.js + ChatKit | Agent Builder integration, web component | ChatKit UI development |
| mastra-overview | Mastra framework | Multi-LLM orchestration | Framework exploration |
| smithery-example | Smithery + FastMCP | MCP playground, development tools | MCP development |
| mcp-apps | MCP Apps (OpenAI Apps SDK) | Example MCP Apps (weather + stock analysis) | MCP Apps reference implementations |
Project Descriptions
agent2agent/
Investment Research Analyst Agent
A production-ready investment research agent implementing Google's Agent-to-Agent (A2A) protocol for remote agent communication.
Key Features:
- Framework: LangGraph with LangChain
- Protocol: Agent-to-Agent (A2A) for remote communication
- Integration: Slack with Block Kit UI and metadata modals
- Architecture: FastAPI server exposing both A2A endpoints and Slack events
- Memory: Persistent conversation state management
- Deployment: Docker ready with Render.com configuration
Technical Stack:
- LangGraph for agent orchestration
- FastAPI for A2A protocol implementation
- Slack Block Kit for interactive UI
- LangSmith for observability (optional)
- Docker for containerized deployment
Use Cases:
- Stock summaries and analysis
- SEC filings research
- Analyst recommendations
- Financial data aggregation
- Investment research workflows
mcp-financial/
Investment Analyst MCP Agent
A financial data agent powered by FastMCP with ASGI integration, providing both CLI and Slack interfaces.
Key Features:
- Framework: FastMCP with FastAPI ASGI integration
- Interfaces: CLI client and Slack bot
- Architecture: MCP server exposed via FastAPI endpoints
- Integration: Direct Slack event handling
- Deployment: Production-ready with health checks
Technical Stack:
- FastMCP for Model Context Protocol implementation
- FastAPI for ASGI integration
- Uvicorn for server runtime
- Slack API for bot functionality
- MCP Inspector for debugging
Use Cases:
- Financial data analysis
- Stock price monitoring
- Earnings analysis
- Market research
- Investment insights
bright-mcp-server-overview/
Bright Data MCP Research Agent
A comprehensive research agent powered by Bright Data's web scraping infrastructure, featuring dual AI agent implementations.
Key Features:
- Dual Framework: LangGraph (with memory) + Google ADK (with extended timeouts)
- Integration: Bright Data MCP server for web scraping
- Slack Interface: Interactive agent selection via dropdown
- Memory: Persistent conversation memory (LangGraph)
- Timeouts: Extended timeout handling (ADK) for long operations
- Specialization: SEO research, e-commerce intelligence, market analysis
Technical Stack:
- LangGraph Agent: OpenAI GPT with MemorySaver checkpointer
- ADK Agent: Google Gemini 2.0 Flash with custom timeout patches
- MCP Integration: Bright Data MCP server for data collection
- Slack Integration: Bot with agent selection and interactive UI
Agent Comparison:
| Feature | LangGraph Agent | ADK Agent |
|---|---|---|
| Memory | Persistent (checkpointer) | Context-aware (5 messages) |
| Timeout | Standard (5s) | Extended (60s) |
| Model | OpenAI GPT | Gemini 2.0 Flash |
| Best For | Interactive conversations | Long-running operations |
Use Cases:
- SEO keyword research and SERP analysis
- E-commerce product monitoring and price tracking
- Competitor analysis and market intelligence
- Web scraping and data collection
- Business intelligence and insights
fpl-deepagent/
Fantasy Premier League MCP Assistant
A comprehensive Fantasy Premier League assistant that integrates with ChatGPT through the Model Context Protocol (MCP), featuring beautiful React UI components and real-time FPL data.
Key Features:
- Framework: FastMCP with Streamable HTTP transport
- UI Integration: React 18 + TypeScript components for ChatGPT
- Real-time Data: Live FPL API integration with caching and error handling
- Design Compliance: Follows OpenAI Apps SDK design guidelines exactly
- Interactive Tools: Player search, detailed stats, and side-by-side comparison
Technical Stack:
- FastMCP for MCP server implementation
- React 18 + TypeScript for UI components
- OpenAI Apps SDK integration with
window.openaiAPI - esbuild for fast, modern bundling
- Streamable HTTP for bidirectional communication
UI Components:
- PlayerListComponent: Interactive player grid with favorites
- PlayerDetailComponent: Detailed player stats and upcoming fixtures
- PlayerComparisonComponent: Side-by-side comparison with highlighted stats
Use Cases:
- Player search and discovery
- Detailed player statistics and form analysis
- Player comparison for team selection
- FPL team optimization
- Real-time price and form tracking
task-manager-app/
Task Manager ChatGPT App (Apps SDK + MCP + Supabase + OAuth)
A production-ready tutorial showing how to build a ChatGPT App with:
- FastMCP (Streamable HTTP) as the MCP server
- React widgets rendered inside ChatGPT
- Supabase (Postgres) as authoritative state for tasks/notifications
- OAuth (Auth0) for multi-user authentication (MCP OAuth)
- Optional Slack notifications (send now + schedule)
Start here:
task-manager-app/README.md
notion-mcp-agent/
Notion Knowledge Management Agent
A sophisticated agent that integrates with Notion through MCP, providing intelligent database management and knowledge organization capabilities.
Key Features:
- Framework: LangGraph with MCP integration
- Integration: Notion API for database operations
- Slack Interface: Interactive knowledge management
- Context Management: Intelligent data aggregation
- Database Operations: Create, read, update, and organize Notion databases
Technical Stack:
- LangGraph for agent orchestration
- Notion MCP server for database operations
- Slack API for user interaction
- Context aggregation for intelligent responses
Use Cases:
- Knowledge base management
- Database organization and maintenance
- Content aggregation and structuring
- Team collaboration workflows
- Information retrieval and organization
claude-advanced-tool-use/
Claude Advanced Tool Use Tutorial
A comprehensive tutorial demonstrating Anthropic's Advanced Tool Use features: Programmatic Tool Calling (PTC) and Tool Search. These features enable AI agents to scale to thousands of tools while dramatically reducing token usage.
Key Features:
- Programmatic Tool Calling (PTC): Claude writes Python code that orchestrates tool calls in a sandbox
- Tool Search: Dynamic tool discovery with
defer_loadingfor efficient context usage - MCP Integration: Tool Search combined with MCP servers via
mcp_toolset - Real-World Examples: Financial data tools using yfinance
- Token Savings: Up to 98% reduction in token usage for complex tasks
Technical Stack:
- Anthropic Claude API (Sonnet 4.5)
- Beta headers:
advanced-tool-use-2025-11-20 - FastMCP for MCP server implementation
- Python + yfinance for financial data
- ngrok for MCP server tunneling
Examples:
01_ptc_token_savings.py- Programmatic Tool Calling with token comparison02_tool_search.py- Tool Search with 10 deferred financial tools03_mcp_tool_search.py- MCP + Tool Search via ngrok tunnelmcp_server.py- FastMCP server exposing financial tools
Key Concepts:
| Feature | Description | Token Savings |
|---|---|---|
| Programmatic Tool Calling | Tool results stay in sandbox, only print() output enters context | 37% |
| Tool Search | Only load tool definitions when discovered | 85% |
| Combined | PTC + Tool Search together | Up to 98% |
Use Cases:
- Building AI agents with many tools (100+)
- Reducing context window bloat from tool definitions
- Processing large datasets without context overflow
- MCP server integration with dynamic tool discovery
- Token-efficient financial analysis agents
claude-skills/
Claude Skills API Implementation
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Adoption & maintenance
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Last commit
7mo ago
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CategoryFinance & Fintech
5/5 checks healthy over the last 45d
33Quality signal: Emerging · 33/100How this signal is calculated ▾
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