XBRL-US MCP Server
Provides secure access to XBRL-US financial data with session-based authentication, enabling users to search for companies by fiscal year and retrieve their financial facts from SEC filings.
README
XBRL-US MCP Server
A Model Context Protocol (MCP) server that provides secure access to XBRL-US financial data with session-based authentication and state persistence.
Features
- Session-Based Authentication: Efficient session management with automatic token reuse
- State Persistence: XBRL instances persist across multiple tool calls within the same session
- Company Search: Search for companies by fiscal year and retrieve financial facts
- Secure Credentials: SHA256-hashed credential validation and secure storage
Tools Available
Search Companies
Search for companies by fiscal year and retrieve financial facts.
- Parameters:
year(integer): Fiscal year to search forlimit(optional, default: 10): Maximum number of results to return
- Returns: List of financial facts for companies in the specified year
Authentication
This server requires XBRL-US API credentials provided via URL parameters:
- Username: Your XBRL-US account username
- Password: Your XBRL-US account password
- Client ID: Your XBRL-US API client ID
- Client Secret: Your XBRL-US API client secret
Configuration Format
Credentials are passed as a base64-encoded JSON object in the config URL parameter:
# Example configuration object (before base64 encoding):
{
"username": "your-xbrl-username",
"password": "your-xbrl-password",
"client_id": "your-client-id",
"client_secret": "your-client-secret"
}
Installation & Setup
Prerequisites
- Python 3.13+
- XBRL-US API account and credentials
- uv (for dependency management)
Local Development
- Clone the repository:
git clone <repository-url>
cd xbrl-us-mcp
- Install dependencies:
uv sync
- Run the server:
uv run playground
The server will start on port 8081 by default and open smithery.ai playground
Usage Example
Search for Companies in 2023
Tool: search_companies
Parameters: {"year": 2023, "limit": 10}
This will return financial facts for companies with data available for fiscal year 2023.
Architecture
Session Management
The server implements sophisticated session management:
- FastMCP Session IDs: Uses FastMCP's built-in session identification
- Session-Scoped Storage: XBRL instances persist across requests within the same session
- Automatic Token Reuse: Reuses valid XBRL authentication tokens to improve performance
- Credential Validation: SHA256 hashing ensures secure credential comparison
- Token Expiration: Automatically handles expired tokens and re-authenticates when needed
Project Structure
xbrl-us-mcp/
├── src/
│ ├── index.py # Main FastMCP server
│ └── funcs/
│ ├── __init__.py
│ └── middleware.py # Session authentication middleware
├── smithery.yaml # Deployment configuration
├── pyproject.toml # Python dependencies
└── README.md # This file
Session Persistence Benefits
- Performance: Eliminates redundant authentication calls
- Efficiency: Reuses XBRL instances across multiple tool calls
- Reliability: Handles token expiration gracefully
- Security: Secure credential hashing and validation
Expected Behavior
First Request in Session:
New XBRL instance created for session abc123...: token...
Subsequent Requests in Same Session:
Reusing valid XBRL session for abc123...
Reusing XBRL instance: token...
Error Handling
The server provides detailed error messages for:
- Missing or invalid credentials
- Authentication failures
- Token expiration
- Network connectivity issues
- Invalid search parameters
Security Features
- Credential Hashing: SHA256 hashing of credentials for secure comparison
- Session Isolation: Each session maintains independent authentication state
- Token Validation: Automatic validation of XBRL token expiration
- Secure Storage: Credentials are never stored in plain text
Contributing
- Fork the repository
- Create a feature branch
- Make your changes
- Submit a pull request
License
This project is licensed under the MIT License.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。