How do I install Edgar?

Edgar is a local plugin. Install it using PyPI package: mcpwright-edgar and add the generated configuration snippet to your AI app's MCP config file. Then restart your AI app.

What credentials does Edgar need?

Edgar requires the following credentials or environment variables: EDGAR_MCP_USER_AGENT. You can find setup instructions on the server detail page.

What AI apps work with Edgar?

Edgar uses the Model Context Protocol (MCP) and works with any MCP-compatible AI app, including Claude, ChatGPT / Codex, Gemini, Copilot, Cursor, and more.

Edgar MCP Server - SEC filings in your agent: issuers, Reg CF/D/A raises, XBRL

About

SEC filings in your agent: issuers, Reg CF/D/A raises, XBRL financials, insider trades.

Security Report

10.0

Low Risk10.0Low Risk

Valid MCP server (1 strong, 3 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.

5 files analyzed · 1 issue found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

env_vars

Check that this permission is expected for this type of plugin.

What You'll Need

Set these up before or after installing:

Descriptive User-Agent with contact info, which the SEC requests (e.g. 'your-app you@example.com'). Optional but recommended.Optional

Environment variable: EDGAR_MCP_USER_AGENT

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-mcpwright-edgar-mcp": {
      "env": {
        "EDGAR_MCP_USER_AGENT": "your-edgar-mcp-user-agent-here"
      },
      "args": [
        "mcpwright-edgar"
      ],
      "command": "uvx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

edgar-mcp

SEC EDGAR filings, inside your agent. An MCP server that lets an LLM resolve companies, search filings, and pull recent securities offerings straight from the SEC — built on Anthropic's official mcp Python SDK.

All tools are read-only and hit public SEC endpoints (no API key required).

Status: 11 tools, working today (see below). Published on PyPI as mcpwright-edgar and in the official MCP Registry. See the roadmap for what's next.

Tools

Tool	What it does
`lookup_issuer(query, limit=10)`	Resolve a ticker or company name → CIK, legal name, tickers, exchange. Works for exchange-listed and private / non-exchange filers (Reg CF / Reg A issuers, funds).
`list_filings(cik_or_query, form_type=None, limit=20)`	An issuer's most recent filings, newest first. Optional form-type filter (e.g. `10-K`, `C`, `D`).
`search_filings(query, forms=None, date_from=None, date_to=None, limit=20)`	Full-text search across filing documents.
`get_recent_offerings(form="C", since=None, state=None, limit=20)`	Recent securities offerings, newest first — `form="C"` (Reg CF), `"D"` (Reg D), or `"A"` (Reg A), optionally filtered by issuer `state` (e.g. `"CA"`).
`get_filing(accession_or_url, cik=None)`	Open one filing: form, filing date, primary-document link, and every document in the filing.
`get_form_d_details(accession_or_url, cik=None)`	Parse a Form D (Reg D) raise: offering amount, sold/remaining, min investment, # investors, industry, revenue range, security types, exemptions, and the officers/directors/promoters.
`get_form_c_details(accession_or_url, cik=None)`	Parse a Form C (Reg CF) raise: target/max amount, price, security type, deadline, intermediary, employees, and a two-year financial snapshot (revenue, net income, assets, debt).
`get_company_facts(cik_or_query)`	Headline financials from a public company's XBRL facts: latest annual revenue, gross/operating income, net income, assets, liabilities, equity, cash.
`get_filing_text(url, offset=0, max_chars=20000)`	Fetch a document's text (HTML stripped) for reading/summarizing — paginated, since filings can exceed 1M characters.
`get_insiders(cik_or_query, limit=25)`	A company's insiders (officers, directors, >10% owners) from recent Section 16 filings, with roles.
`get_insider_trades(cik_or_query, limit=20)`	Recent insider transactions (Form 4): owner, role, buy/sell/grant, shares, price, shares owned after.

Install

Requires Python 3.12+. The zero-clone way to run it (the PyPI package is mcpwright-edgar; the command, server, and tools are all "edgar"):

uvx mcpwright-edgar

Claude Code

claude mcp add edgar -- uvx mcpwright-edgar

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "edgar": { "command": "uvx", "args": ["mcpwright-edgar"] }
  }
}

OpenAI Agents SDK (Python)

It's a standard MCP server, so it works with any MCP-capable client — not just Claude. With the OpenAI Agents SDK:

from agents import Agent, Runner
from agents.mcp import MCPServerStdio

async def main():
    async with MCPServerStdio(
        name="edgar",
        params={
            "command": "uvx",
            "args": ["mcpwright-edgar"],
            "env": {"EDGAR_MCP_USER_AGENT": "your-app you@example.com"},
        },
    ) as edgar:
        agent = Agent(
            name="Analyst",
            instructions="Use the EDGAR tools for SEC filings and company data.",
            mcp_servers=[edgar],
        )
        result = await Runner.run(
            agent, "Recent Reg D raises in California — who's behind the biggest?"
        )
        print(result.final_output)

Any other MCP client (Cursor, VS Code, Cline, Goose, Zed, …)

They all launch a stdio MCP server the same way — point yours at:

{
  "mcpServers": {
    "edgar": {
      "command": "uvx",
      "args": ["mcpwright-edgar"],
      "env": { "EDGAR_MCP_USER_AGENT": "your-app you@example.com" }
    }
  }
}

Hosted chat connectors (e.g. ChatGPT connectors) expect a remote MCP server over Streamable HTTP; mcpwright-edgar runs locally over stdio. Running it behind Streamable HTTP for a hosted endpoint is straightforward if you need that.

SEC etiquette: the SEC requires a descriptive User-Agent with contact info and rate-limits to ~10 req/s. Set your own via the EDGAR_MCP_USER_AGENT env var (e.g. "your-app your-email@example.com"). The client throttles and retries for you.

Caching: responses are cached in-memory (byte-budgeted LRU) to cut latency and SEC load — immutable filing-archive content for days, the ticker map for 24h, everything else briefly. Set EDGAR_MCP_CACHE=0 to disable.

Develop

git clone https://github.com/mcpwright/edgar-mcp && cd edgar-mcp
uv sync
uv run pytest                       # tests (mocked SEC responses)
uv run ruff check . && uv run ruff format --check .   # lint + format
uv run mypy src tests               # strict type checking
uv run mcp dev src/edgar_mcp/server.py   # poke the tools in the MCP Inspector

Roadmap

Questions & feedback

Questions, ideas, or "could it do X?" → Discussions
Bugs & concrete feature requests → Issues

Contributions welcome — and if you build something with it, I'd love to hear about it.

Part of mcpwright · built by Devender Gollapally

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Edgar MCP Server