SEC EDGAR MCP Server

The SEC EDGAR MCP server solves a common bottleneck for financial analysts, researchers, and AI developers: the difficulty of programmatically accessing and querying the vast archive of U.S. Securities and Exchange Commission filings. By exposing the SEC’s EDGAR database as a first‑class MCP resource, it allows language models to retrieve real‑time, authoritative corporate data without manual scraping or reliance on third‑party APIs. This eliminates latency and potential inaccuracies that arise when parsing unstructured web pages, ensuring that AI agents can base their reasoning on the exact figures published by companies themselves.

At its core, the server integrates with the EdgarTools Python library to fetch filings directly from SEC servers and performs native XBRL parsing. This means that when an assistant asks for “Apple’s latest 10‑K revenue,” the MCP server can return the precise figure from the official filing, complete with reference identifiers and timestamps. Developers benefit from this precision because it removes the need to build custom parsers for each filing type, saving time and reducing error risk.

Key capabilities include:

• File retrieval: Pull the most recent or specific filings (10‑K, 10‑Q, 8‑K, etc.) for any ticker or CIK.
• Metric extraction: Query exact financial line items (revenues, net income, assets) directly from XBRL tags.
• Search and filtering: Find filings by date range, form type, or keyword within the document.
• Metadata access: Retrieve filing metadata such as submission dates, company details, and filing status.

Typical use cases span from investment research—where an AI agent can quickly compare quarterly earnings across multiple firms—to regulatory compliance tools that automatically flag filings with unusual disclosures. Because the MCP server adheres to the open Model Context Protocol, any LLM platform or custom assistant can discover and invoke these tools without bespoke integration code.

The server’s unique advantage lies in its combination of real‑time access to the official SEC data and exact XBRL parsing, which ensures that AI assistants can provide answers that are both up‑to‑date and mathematically precise. This level of fidelity is especially valuable in high‑stakes financial decision making, where even a small data error can lead to significant consequences.