hollaugo-financial-research-mcp-server

@smithery/hollaugo-financial-research-mcp-server

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0 Stars 4 次浏览 smithery 更新于 2026-08-23

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

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该服务暂未提供标准配置，请参考 README 手动接入

可用工具（5 个）

该服务在 MCP 协议中暴露的工具，AI 可按需调用

tavily_search 14 个参数需填 1 项 Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.

Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.

必填参数：query

tavily_extract 6 个参数需填 1 项 Extract content from URLs. Returns raw page content in markdown or text format.

Extract content from URLs. Returns raw page content in markdown or text format.

必填参数：urls

tavily_crawl 11 个参数需填 1 项 Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.

Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.

必填参数：url

tavily_map 8 个参数需填 1 项 Map a website's structure. Returns a list of URLs found starting from the base URL.

Map a website's structure. Returns a list of URLs found starting from the base URL.

必填参数：url

tavily_research 2 个参数需填 1 项 Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources to answer a question or complete a task. Returns a detailed response based on the research findings.

Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources to answer a question or complete a task. Returns a detailed response based on the research findings.

必填参数：input

服务介绍

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 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

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

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

#  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

查看源码项目主页

可用工具 （5 个）

服务介绍

AI Agent Tutorials & Implementations

# Repository Overview

# Project Descriptions

# # agent2agent/

# # mcp-financial/

# # bright-mcp-server-overview/

# # fpl-deepagent/

# # task-manager-app/

# # notion-mcp-agent/

# # claude-advanced-tool-use/

# # claude-skills/

# # openai-chatkit-starter-app/

# # mastra-overview/

# # smithery-example/

# # mcp-apps/

# Getting Started

# # Common Requirements

# # Quick Start Pattern

# Architecture Patterns

# # Model Context Protocol (MCP)

# # Agent Communication

# # UI Integration Patterns

# # Document Generation

# # Development & Testing

# # Integration Patterns

# Contributing

# License

# Support & Resources

# # Documentation Links

# # Platform-Specific Support

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