ai.smithery/hollaugo-financial-research-mcp-server

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by hollaugo · Python

Analyze stocks with summaries, price targets, and analyst recommendations. Track SEC filings, divi…

Analyze stocks with summaries, price targets, analyst recommendations, and SEC filings.

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.openai API
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_loading for 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 comparison
02_tool_search.py - Tool Search with 10 deferred financial tools
03_mcp_tool_search.py - MCP + Tool Search via ngrok tunnel
mcp_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

A comprehensive implementation of Claude's Skills API for automated document generation and custom skill creation.

Key Features:

Framework: Claude Skills API with streaming support
Document Generation: PowerPoint, Excel, Word, and PDF creation
Custom Skills: Upload and manage custom skills (8MB limit)
File Management: List, download, and delete generated files
Multi-Skill Workflows: Combine multiple skills in single requests

Technical Stack:

Claude Skills API with beta features
Code execution environment (2025-08-25)
Files API (2025-04-14)
Streaming responses for real-time progress
Python SDK with uv package manager

Utilities:

list-skills.py - List all available skills
create-skill.py - Upload custom skills from directories
use-skill.py - Generate documents with single skills
multi-skill-demo.py - Complex workflows with multiple skills
list-files.py / download-file.py / delete-file.py - File management

Use Cases:

Automated PowerPoint presentation generation
Excel spreadsheet creation and data analysis
Word document generation
PDF report creation
Custom skill development and deployment
Multi-format document workflows

openai-chatkit-starter-app/

ChatKit Web Component Starter

A minimal Next.js starter template for building ChatKit applications with OpenAI's Agent Builder workflows.

Key Features:

Framework: Next.js with ChatKit web component
Integration: OpenAI Agent Builder workflows
Customization: Configurable themes, prompts, and UI
Session Management: Ready-to-use session endpoint
Deployment: Domain allowlist verification support

Technical Stack:

Next.js for application framework
OpenAI ChatKit web component (<openai-chatkit>)
OpenAI API integration
TypeScript for type safety
Configurable theming system

Key Components:

Session creation endpoint (/api/create-session)
ChatKit panel with event handlers
Theme and color scheme controls
Starter prompts configuration
Error overlay for debugging

Use Cases:

ChatKit application prototyping
Agent Builder workflow integration
Custom ChatKit UI development
OpenAI workflow testing
Production ChatKit deployments

mastra-overview/

Mastra Framework Exploration

An exploration of the Mastra framework for multi-LLM orchestration and agent management.

Key Features:

Framework: Mastra for multi-LLM orchestration
Multi-LLM: Support for multiple language models
Orchestration: Intelligent model selection and routing
Polyfills: Crypto polyfills for browser compatibility

Technical Stack:

Mastra framework
Multi-LLM integration
Browser compatibility polyfills
TypeScript configuration

Use Cases:

Multi-LLM agent systems
Model orchestration and routing
Framework exploration and evaluation
LLM comparison and benchmarking

smithery-example/

MCP Development Playground

A comprehensive development environment for MCP (Model Context Protocol) with FastMCP integration and testing tools.

Key Features:

Framework: Smithery + FastMCP
Development Tools: MCP playground and testing environment
Financial Integration: Example financial server implementation
Testing: Comprehensive test suite and examples
Documentation: Development guides and examples

Technical Stack:

Smithery for MCP development
FastMCP for server implementation
Testing frameworks for validation
Development tooling and playgrounds

Use Cases:

MCP server development
Protocol testing and validation
Financial data integration examples
Development environment setup
MCP learning and exploration

mcp-apps/

MCP Apps Examples (Weather + Stock Analysis)

Two minimal example MCP Apps showing how to build UI + server experiences using the MCP Apps extensions.

Key Features:

Weather App: UI + MCP server example with a simple weather workflow
Stock Analysis App: UI + MCP server example for market/stock analysis
Apps SDK: Designed to follow MCP Apps extension patterns
Docs Reference: See the MCP Apps docs for the full guide

Use Cases:

Learning MCP Apps fundamentals
Building UI-backed MCP Apps
Reference implementations for new MCP App projects

Getting Started

Each project includes comprehensive setup instructions in its respective README file. General prerequisites include:

Common Requirements

Python 3.9+ (some projects require newer; see each project README)
Valid API keys for respective services
Slack workspace access (for Slack integrations)
Environment variable configuration

Quick Start Pattern

bash

# 1. Navigate to desired project
cd [project-name]/

# 2. Install dependencies
# Most Python projects here use uv:
uv sync
# Some projects use pip/requirements.txt:
# pip install -r requirements.txt

# 3. Configure environment
cp .env.example .env
# Edit .env with your API keys

# 4. Run the agent
# (varies by project - see individual READMEs)

Architecture Patterns

Model Context Protocol (MCP)

Multiple projects demonstrate different MCP implementation patterns:

FastMCP ASGI: Direct FastAPI integration (mcp-financial, smithery-example)
FastMCP Streamable HTTP: Modern bidirectional communication (fpl-deepagent)
Bright Data MCP: External MCP server communication
Notion MCP: Database and knowledge management integration

Agent Communication

A2A Protocol: Remote agent-to-agent communication (agent2agent)
State Management: Persistent conversation memory (bright-mcp-server-overview)

UI Integration Patterns

React + ChatGPT: OpenAI Apps SDK integration (fpl-deepagent)
Next.js + ChatKit: Agent Builder workflow integration (openai-chatkit-starter-app)
Slack Bots: Event-driven chat interfaces (multiple projects)
CLI Clients: Command-line agent interaction

Document Generation

Claude Skills API: Automated document creation with streaming (claude-skills)
Multi-Format Support: PowerPoint, Excel, Word, PDF generation
Custom Skills: Uploadable skill packages for specialized tasks

Development & Testing

MCP Playground: Development and testing environment (smithery-example)
Multi-LLM Orchestration: Framework exploration (mastra-overview)
Agent Builder: OpenAI workflow development (openai-chatkit-starter-app)

Integration Patterns

Container Deployment: Docker and cloud-ready
API Integration: RESTful agent endpoints
Database Integration: Knowledge management systems
Real-time Data: Live API integration with caching

Contributing

Each project welcomes contributions. Please:

Fork the repository
Create a feature branch
Follow the project's coding standards
Include tests where applicable
Submit a Pull Request

License

MIT License - see individual project LICENSE files for details.

Support & Resources

Documentation Links

Platform-Specific Support

Bright Data: brightdata.com/support
Notion: developers.notion.com
Fantasy Premier League: fpl.readthedocs.io
Slack: api.slack.com/support

Built with ❤️ demonstrating the future of AI agent development

Install

claude_desktop_config.json

{
  "mcpServers": {
    "hollaugo-financial-research-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://server.smithery.ai/@hollaugo/financial-research-mcp-server/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.