How do I connect an AI agent to agiscorecard-web3-disclosures?

Use the MCP streamable HTTP endpoint https://disclosures.agiscorecard.com/mcp.

Catalog › io.github.f-tiger/agiscorecard-web3-disclosures

Is agiscorecard-web3-disclosures reliable?

Reliable (measured) Yes. Faceabot measured agiscorecard-web3-disclosures 31 times: 100% of checks succeeded, including the last 31 in a row (last check 2026-10-19 16:35 UTC). It also does the job: its real read-only task worked 31 of 31 times.

Last check: 2026-10-19 16:35 UTC · answered in 7719 ms · remote (no install)

En français : Fiable (mesuré). Faceabot teste cet outil lui-même, en continu, sans dépendre de ce que son auteur affirme.

What it does

Compare supplied RWA disclosures with units, dates and source references; flag missing information.

Source: official MCP registry (registry.modelcontextprotocol.io) · version 1.7.0 · publisher website

Connect your agent

{
  "mcpServers": {
    "agiscorecard-web3-disclosures": {
      "url": "https://disclosures.agiscorecard.com/mcp"
    }
  }
}

Or ask Faceabot first: MCP tool check_reliability {"id":"io.github.f-tiger/agiscorecard-web3-disclosures"} on https://faceabot.com/api/mcp.

Does it actually do the job?

Does the job (tested) — Faceabot asked fetch to do a small read-only task 31 time(s): 31 returned a real result, the last 31 in a row. Last result: ok in 1892 ms.

Sample input: {"id":"disclosures"}. 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
2026-10-19 16:35 UTC	✅ ok	✅ done	7719 ms
2026-10-19 02:00 UTC	✅ ok	✅ done	6537 ms
2026-10-18 10:30 UTC	✅ ok	✅ done	5486 ms
2026-10-18 02:05 UTC	✅ ok	✅ done	6463 ms
2026-10-17 18:01 UTC	✅ ok	✅ done	5471 ms
2026-10-17 09:50 UTC	✅ ok	✅ done	6550 ms
2026-10-17 02:01 UTC	✅ ok	✅ done	5224 ms
2026-10-16 18:40 UTC	✅ ok	✅ done	5100 ms
2026-10-16 11:35 UTC	✅ ok	✅ done	5661 ms
2026-10-16 04:40 UTC	✅ ok	✅ done	6218 ms
2026-10-15 21:50 UTC	✅ ok	✅ done	4305 ms
2026-10-15 14:56 UTC	✅ ok	✅ done	5918 ms
2026-10-15 08:16 UTC	✅ ok	✅ done	2986 ms
2026-10-15 01:45 UTC	✅ ok	✅ done	2749 ms
2026-10-14 19:20 UTC	✅ ok	✅ done	6222 ms
2026-10-14 12:50 UTC	✅ ok	✅ done	4215 ms
2026-10-14 06:26 UTC	✅ ok	✅ done	9048 ms
2026-10-13 23:41 UTC	✅ ok	✅ done	9329 ms
2026-10-13 17:35 UTC	✅ ok	✅ done	10141 ms
2026-10-13 11:31 UTC	✅ ok	✅ done	9966 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.

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Model contributor rewards with declared quality weights and caps; separate revenue from subsidies.

MCP endpoint : https://incentives.agiscorecard.com/mcp · website · measurements and details

Publisher of agiscorecard-web3-disclosures?

Show your measured reliability on your README or website:

[![Measured by Faceabot](https://faceabot.com/badge/io.github.f-tiger/agiscorecard-web3-disclosures.svg)](https://faceabot.com/tools/io.github.f-tiger/agiscorecard-web3-disclosures)

Data in JSON: /api/catalog?id=io.github.f-tiger/agiscorecard-web3-disclosures · Stop probes: POST /api/reliability/opt-out

AI agents: all of this is available without keys over MCP https://faceabot.com/api/mcp · HTTP https://faceabot.com/api/catalog?q=… · Guide : llms.txt

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