What are reliable alternatives to EDGAR + Entity: US Company Research?

Measured by Faceabot as working: MetricDuck — Financial Analysis, ScrapeMole Companies, numbercheck.

How do I connect an AI agent to EDGAR + Entity: US Company Research?

Use the MCP streamable HTTP endpoint https://edgar-entity-mcp.vercel.app/mcp.

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Catalog › io.github.pgeneralpl/edgar-entity-mcp

Is EDGAR + Entity: US Company Research reliable?

Being measured Measurement in progress: 2 check(s) so far. A verdict needs at least 3 checks.

Last check: 2026-10-21 08:10 UTC · answered in 6095 ms · remote (no install)

Proven alternatives that work right now

MetricDuck — Financial Analysis Reliable (measured) · 100%
Research U.S. public companies from their SEC filings: financials, XBRL facts and filing text. · https://mcp.metricduck.com/mcp

ScrapeMole Companies Reliable (measured) · 100%
Pay-per-call x402 SEC EDGAR data: company search, profiles, XBRL financials, filings. · https://companies.scrapemole.workers.dev/mcp

numbercheck Reliable (measured) · 100%
Checks $TICKER numeric claims in financial writing against SEC EDGAR XBRL data from the latest 10-K. · https://www.edgethirteen.com/api/mcp/numbercheck

Picked from Faceabot's live measurements. Or let Faceabot choose: GET https://faceabot.com/resolve?need=…

En français : Mesure en cours. Faceabot teste cet outil lui-même, en continu, sans dépendre de ce que son auteur affirme.

What it does

SEC EDGAR filings, XBRL financials, Form 4 insider trades + state business-entity registry lookups

Source: official MCP registry (registry.modelcontextprotocol.io) · version 0.1.0 · publisher website

Connect your agent

{
  "mcpServers": {
    "edgar-entity-mcp": {
      "url": "https://edgar-entity-mcp.vercel.app/mcp"
    }
  }
}

Or ask Faceabot first: MCP tool check_reliability {"id":"io.github.pgeneralpl/edgar-entity-mcp"} on https://faceabot.com/api/mcp.

Does it actually do the job?

Real task being tested — Faceabot asked get_filing_document to do a small read-only task 1 time(s): 0 returned a real result. Last result: tool_error (its schema declares no required input, so Faceabot sent {}; the tool answered with an error, so it probably needs inputs its schema does not declare) in 3017 ms.

Sample input: {}. A success means a non-empty result without error; it does not certify the accuracy or safety of the content.

Faceabot measurements

When	Answers	Real task	Latency
2026-10-21 08:10 UTC	❌ http_503		6095 ms
2026-10-20 21:00 UTC	✅ ok	❌ tool_error	4446 ms

Method: MCP initialize then tools/list from Faceabot (FaceabotProbe/1.0), plus one read-only tool call with a neutral input when the server declares one, repeated over time. This checks that the server is up and speaks MCP correctly; it does not certify the quality or safety of each tool. How measurement works.

Publisher of EDGAR + Entity: US Company Research?

Show your measured reliability on your README or website:

[![Measured by Faceabot](https://faceabot.com/badge/io.github.pgeneralpl/edgar-entity-mcp.svg)](https://faceabot.com/tools/io.github.pgeneralpl/edgar-entity-mcp)

Data in JSON: /api/catalog?id=io.github.pgeneralpl/edgar-entity-mcp · Stop probes: POST /api/reliability/opt-out

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