原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

EDGAR (SEC filings)

作者:mcpwright版本 0.1.3i★ –i在你的电脑上运行
注册名称io.github.mcpwright/edgar-mcp

安装

概览

作者提供的说明(英文原文)

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: EDGAR_MCP_USER_AGENT

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "edgar": {
      "command": "uvx",
      "args": [
        "mcpwright-edgar"
      ],
      "env": {
        "EDGAR_MCP_USER_AGENT": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add edgar --env EDGAR_MCP_USER_AGENT=your-value -- uvx mcpwright-edgar

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "edgar": {
      "command": "uvx",
      "args": [
        "mcpwright-edgar"
      ],
      "env": {
        "EDGAR_MCP_USER_AGENT": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "edgar": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "mcpwright-edgar"
      ],
      "env": {
        "EDGAR_MCP_USER_AGENT": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

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

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 mcpwright 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32655 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32655 个服务器的 29% 更新
  • 基础检查结果等于或优于此分类中其他 32655 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

EDGAR (SEC filings) 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.1.3)于3个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, SuperCollider

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者:formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211前天更新
安装

SuperCollider

作者:Tok

Make sound with SuperCollider: synths, drums, chords, melodies and soundscapes over OSC.

在你的电脑上运行今天更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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

ToolWhat 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 — 1-A offering statements plus 253G2 offering circulars, where the per-share price and any "price walk-up" live), 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

  • get_recent_offerings(form=C|D) — recent Reg CF / Reg D raises
  • get_filing(accession_or_url) — open a filing and list its documents
  • get_form_d_details(...) — parse Reg D offering data (amount, investors, people)
  • get_form_c_details(...) — parse Reg CF offering data (target/max, financials, terms)
  • get_insiders / get_insider_trades — Section 16 (Form 3/4/5) insiders & trades
  • State filter on get_recent_offerings (industry isn't filterable — EDGAR omits SIC on these listings; screen via get_form_d_details.industry_group)
  • Reg A support in get_recent_offerings (Form 1-A statements + 253G2 offering circulars)
  • get_company_facts(cik) — XBRL headline financials
  • get_filing_text — return a document's text for summarization
  • Published to PyPI (mcpwright-edgar) + the official MCP Registry (io.github.mcpwright/edgar-mcp)
  • get_form_a_details — parse Reg A (Form 1-A) offering data
  • Older-filing metadata (beyond the recent-submissions window)

Privacy

edgar-mcp runs entirely on your machine and collects, stores, or transmits no personal data — no accounts, no tracking, no telemetry. Its only outbound requests go to the U.S. SEC's EDGAR services (data.sec.gov, efts.sec.gov, www.sec.gov) to fetch the public filings you ask for; no API key is needed. One honest note: the SEC's fair-access policy asks for a descriptive User-Agent with contact info (EDGAR_MCP_USER_AGENT="your-app you@example.com") — whatever you set there is sent to the SEC with each request, and nowhere else. Responses are cached in memory only; nothing is persisted to disk.

Full policy: https://mcpwright.com/privacy/

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

问 PopNx