SEC EDGAR MCP Server

What does “SEC EDGAR” MCP Server do?

The SEC EDGAR MCP (Model Context Protocol) Server is a specialized tool designed to connect AI assistants with the U.S. Securities and Exchange Commission’s EDGAR (Electronic Data Gathering, Analysis, and Retrieval) system. By interfacing with external data sources like EDGAR, this MCP server enables AI agents and developers to access, query, and analyze public financial filings and corporate disclosures. This capability enhances development workflows for finance, compliance, and research applications—allowing tasks such as automated financial data extraction, real-time monitoring of filings, and integration into data-driven applications. With the SEC EDGAR MCP Server, developers can efficiently automate and standardize their interactions with EDGAR, saving time and ensuring up-to-date information is always accessible.

List of Prompts

No prompt templates are mentioned in the available documentation.

List of Resources

No explicit resources are described in the available documentation.

List of Tools

No specific tools are listed in the available documentation or in the visible code structure (such as a server.py).

Use Cases of this MCP Server

• Automated Financial Data Extraction: Developers can automate the retrieval of SEC filings and disclosures for integration into financial models, dashboards, or compliance tools.
• Real-Time Filings Monitoring: Useful for analysts and compliance teams to receive updates or trigger workflows based on newly filed SEC documents.
• Data-Driven Research Applications: Enables AI agents to access and process historical or real-time EDGAR data for academic, market, or regulatory research.
• Integration into Compliance Workflows: Streamlines the process of checking company filings for regulatory compliance within enterprise systems.
• Corporate Intelligence Gathering: Assists due diligence teams and investors in quickly aggregating and analyzing public company disclosures.

How to set it up

Windsurf

1. Ensure you have Python 3.9+ installed.
2. Locate your Windsurf configuration file (usually windsurf.config.json).
3. Add the SEC EDGAR MCP Server using the following JSON snippet:

   {
     "mcpServers": {
       "sec-edgar-mcp": {
         "command": "sec-edgar-mcp",
         "args": []
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify setup by checking the MCP server logs or Windsurf UI.

Claude

1. Install the SEC EDGAR MCP Server (Python 3.9+ required).
2. In Claude’s settings/configuration, add the MCP server as follows:

   {
     "mcpServers": {
       "sec-edgar-mcp": {
         "command": "sec-edgar-mcp",
         "args": []
       }
     }
   }
   

3. Save your settings and restart Claude.
4. Confirm the MCP server is available within the Claude environment.

Cursor

1. Ensure Python 3.9+ is available and the MCP server is installed.
2. Edit Cursor’s configuration file.
3. Add:

   {
     "mcpServers": {
       "sec-edgar-mcp": {
         "command": "sec-edgar-mcp",
         "args": []
       }
     }
   }
   

4. Save and restart Cursor.
5. Validate the connection in the Cursor interface.

Cline

1. Prepare your environment with Python 3.9+.
2. Locate the Cline configuration file.
3. Insert the following:

   {
     "mcpServers": {
       "sec-edgar-mcp": {
         "command": "sec-edgar-mcp",
         "args": []
       }
     }
   }
   

4. Save and restart Cline.
5. Check that the MCP server is now accessible.

Securing API Keys:
If your deployment requires API keys or secrets, use environment variables for security. Example:

{
  "mcpServers": {
    "sec-edgar-mcp": {
      "command": "sec-edgar-mcp",
      "env": {
        "EDGAR_API_KEY": "your_api_key_here"
      },
      "inputs": {}
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "sec-edgar-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “sec-edgar-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the available documentation, the SEC EDGAR MCP provides a clear overview and basic setup instructions, but lacks detail on prompts, resources, and tool definitions, which limits its immediate utility for advanced MCP workflows.

MCP Score

Our opinion:
Given the absence of prompt templates, explicit resource documentation, and tool definitions, but with a clear license and usage context, we would rate this MCP server a 4/10 for completeness and readiness for general use in advanced AI workflows. It is suitable for basic integration but lacks depth for more sophisticated scenarios.