Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
SEC filings in your agent: issuers, Reg CF/D/A raises, XBRL financials, insider trades.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
EDGAR_MCP_USER_AGENTYou need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"edgar": {
"command": "uvx",
"args": [
"mcpwright-edgar"
],
"env": {
"EDGAR_MCP_USER_AGENT": "your-value"
}
}
}
}Run this in your terminal
claude mcp add edgar --env EDGAR_MCP_USER_AGENT=your-value -- uvx mcpwright-edgarFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"edgar": {
"command": "uvx",
"args": [
"mcpwright-edgar"
],
"env": {
"EDGAR_MCP_USER_AGENT": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"edgar": {
"type": "stdio",
"command": "uvx",
"args": [
"mcpwright-edgar"
],
"env": {
"EDGAR_MCP_USER_AGENT": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
EDGAR_MCP_USER_AGENT envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account mcpwright, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.1.3) reached the registry 3 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, Draugr
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Security scanning for AI agents: SAST, SCA, secrets, IaC, DAST, ranked by real risk.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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-edgarand in the
official MCP Registry. See the roadmap for what's next.
| 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 — 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. |
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 mcp add edgar -- uvx mcpwright-edgar
Add to claude_desktop_config.json:
{
"mcpServers": { "edgar": { "command": "uvx", "args": ["mcpwright-edgar"] } } }
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)
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-edgarruns 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-Agentwith contact info and rate-limits
to ~10 req/s. Set your own via the
EDGAR_MCP_USER_AGENTenv 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=0to disable.
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
get_recent_offerings(form=C|D) — recent Reg CF / Reg D raisesget_filing(accession_or_url) — open a filing and list its documentsget_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 & tradesget_recent_offerings (industry isn't filterable — EDGAR omits SIC on these listings; screen via get_form_d_details.industry_group)get_recent_offerings (Form 1-A statements + 253G2 offering circulars)get_company_facts(cik) — XBRL headline financialsget_filing_text — return a document's text for summarizationmcpwright-edgar) + the official MCP Registry (io.github.mcpwright/edgar-mcp)get_form_a_details — parse Reg A (Form 1-A) offering dataedgar-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/
Contributions welcome — and if you build something with it, I'd love to hear about it.
Part of mcpwright · built by Devender Gollapally