Agent Readiness MCP

Name: Agent Readiness MCP
Author: forgemeshlabs

by forgemeshlabsversion 0.2.7i★ –iRuns on your computer

Registry nameio.github.forgemeshlabs/agent-readiness-mcp

Install

Overview

Description from the maker (original English)

Agent Readiness MCP — the free Agent Readiness scanner as an MCP server. Measures your ASO Score.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "agent-readiness": {
      "command": "npx",
      "args": [
        "-y",
        "@forgemeshlabs/agent-readiness-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add agent-readiness -- npx -y @forgemeshlabs/agent-readiness-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "agent-readiness": {
      "command": "npx",
      "args": [
        "-y",
        "@forgemeshlabs/agent-readiness-mcp"
      ]
    }
  }
}

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.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "agent-readiness": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@forgemeshlabs/agent-readiness-mcp"
      ]
    }
  }
}

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.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

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

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account forgemeshlabs, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 18354 servers in this category
Published to the registry more recently than 87% of the other 18354 servers in this category
Basic checks equal to or better than 100% of the other 18354 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Agent Readiness MCP safe?

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.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

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).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.7) reached the registry 6 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Qemu MCP

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Runs on your computer★ 440MITUpdated 14 months ago

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Remote Desktop Commander

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

Runs in the cloud★ 211Updated 2 days ago

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Qemu MCP

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Boot and drive QEMU VMs from an AI agent: keys, screenshots, serial. No guest agent.

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README from the maker

Open the README

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.

agent-readiness-mcp — the free Agent Readiness Scanner

What's your ASO score?

SEO made you visible to search engines. ASO (Agent Signal Optimization) makes you discoverable, trustable, and payable by the AI agents that are becoming the web's next visitors.

agent-readiness-mcp is the free, open-source Agent Readiness MCP — an MCP server that scans any website and produces an Agent Readiness Report scored on the open ASO framework. The beta npm package is @forgemeshlabs/agent-readiness-mcp.

This release tracks Google's current agent-readiness guidance without overstating it: Google Search says traditional SEO fundamentals still apply to generative AI search, llms.txt is ignored by Google Search itself, and browser agents benefit from clean DOM, screenshot, and accessibility-tree signals. The scanner keeps llms.txt because non-Google agents use it, and adds a browser-agent UX check for semantic controls, linked labels, ARIA/role fallbacks, and hidden-overlay risk.

Beta. Experimental ASO scanner for evaluating whether agents can discover, trust, understand, and use a website/API/tool. ASO scoring is experimental and will evolve as agent standards mature.

=== Agent Readiness Report: https://example.com ===

ASO Score: 70/100 Agent Readiness: Ready Level: ASO-4 Trustable — Agents can verify trust, reputation, and operational signals.

Discoverability 20/20 Identity 15/20 Trust 11/15 Commerce 5/15 Reputation 4/15 Memory 15/15

What it checks — 34 signals across 6 pillars

Find gaps in discovery, trust, interoperability, and commerce — every emerging agent standard in one scan:

Pillar / Category	Checks
Discovery	robots.txt, sitemap.xml, Link headers, DNS-AID (`_agent.` ), `/.well-known/ai`
Content	Markdown content negotiation, llms.txt, LLM-readable docs (`/index.md`, `llms-full.txt`)
Bot Access	Explicit AI crawler rules (GPTBot, ClaudeBot, Google-Extended, PerplexityBot…), Content Signals, Web Bot Auth
Interoperability	API Catalog (RFC 9727), OAuth discovery (RFC 8414), OAuth Protected Resource (RFC 9728), auth.md, MCP Server Card (`/.well-known/mcp/server-card.json`), Google A2A Agent Card (`/.well-known/agent-card.json`, required fields validated), Agent Skills, WebMCP
Commerce	x402, MPP, UCP, ACP, machine-readable pricing
Identity & Trust	HTTPS enforcement, JSON-LD/schema.org, agent-friendly browser UX, OpenAPI, agent.json, security.txt, status endpoint, versioning, cross-file identity & signal consistency

Every check returns pass / partial / fail with concrete evidence and a fix recommendation. Results roll up into the six ASO pillars (Discoverability 20, Identity 20, Trust 15, Commerce 15, Reputation 15, Memory 15) → your ASO Score and maturity level.

Install

Requires Node.js ≥ 20. Published on npm as @forgemeshlabs/agent-readiness-mcp — no clone or build needed.

npm install -g @forgemeshlabs/agent-readiness-mcp

Or skip the install entirely and run it with npx (recommended for MCP clients):

npx -y @forgemeshlabs/agent-readiness-mcp

Claude Code

claude mcp add aso -- npx -y @forgemeshlabs/agent-readiness-mcp

Claude Desktop / Cursor / Windsurf (any MCP client)

"mcpServers": { "aso": { "command": "npx", "args": ["-y", "@forgemeshlabs/agent-readiness-mcp"] } } }

Development (from source)

Only needed if you're hacking on the scanner itself:

git clone https://github.com/forgemeshlabs/agent-readiness-mcp

cd agent-readiness-mcp npm install && npm run build claude mcp add aso -- node /path/to/agent-readiness-mcp/dist/index.js

Tools

Tool	What it does
`scan_site`	Full ASO scan → Agent Readiness Report: ASO Score, level, pillar breakdown, all 34 checks with evidence + recommendations
`get_fix_plan`	Prioritized remediation plan with ready-to-paste templates (robots.txt AI rules, llms.txt, agent.json, A2A agent card, MCP server card, x402 manifest, pricing.json, security.txt, status endpoint)
`check_signal`	Run one specific check (e.g. `a2a-agent-card`, `llms-txt`, `x402`)
`list_checks`	Catalog of every check with spec links
`get_aso_framework`	The ASO rubric: pillars, weights, levels, certification thresholds

Use scan_site for a full baseline, check_signal for a single named signal, get_fix_plan for copy-paste remediation, list_checks to discover valid signal IDs, and get_aso_framework to explain the scoring model without scanning a site.

Try it: *"Scan example.com for agent readiness"* · *"What's my ASO score?"* · *"Give me a fix plan to make my site agent-ready."*

CLI smoke test (from a source checkout)

npm run smoke -- https://your-site.com

Glama / registry metadata

This repository includes glama.json for Glama MCP registry ownership and install metadata.

Package: @forgemeshlabs/agent-readiness-mcp
Current release: v0.2.5
Transport: local stdio
Authentication: none required for local stdio use. The scanner does not ask for API keys, tokens, cookies, or third-party credentials.
HTTP deployment: not enabled by this npm package. Any public HTTP deployment of this scanner must add authentication, per-client rate limits, request logging, and an egress policy before exposure.

Recommended Glama/MCP install command:

npx -y @forgemeshlabs/agent-readiness-mcp

Example usage after connecting the server to an MCP client:

Scan https://example.com for agent readiness.

Give me the ASO fix plan for example.com. Check only the llms-txt signal for example.com. List the ASO scanner checks.

Release verification:

Git tag: v0.2.5
npm package: @forgemeshlabs/agent-readiness-mcp
MCP server version: 0.2.6 (read from package.json at startup)

Glama release build

Glama installability requires a Glama release, which is a containerized build created from the Glama Dockerfile admin page, not a GitHub release. This repo includes a production Dockerfile and GLAMA.md with the build spec values to use in Glama:

Build steps:

npm ci

npm run build npm prune --omit=dev

Runtime command:

node dist/index.js

In Glama's CMD arguments field, enter:

["node", "dist/index.js"]

Do not leave CMD arguments as []; Glama validates that field separately from the Dockerfile CMD.

The ASO framework

SEO ranks pages for people. ASO prepares services for agent selection, invocation, payment, and repeat use.

Level	Name	Score
ASO-0	Invisible	0–9
ASO-1	Discoverable	10–29
ASO-2	Understandable	30–49
ASO-3	Invocable	50–69
ASO-4	Trustable	70–89
ASO-5	Autonomous-Commerce-Ready	90–100

Scores from this scanner are directional self-assessments. ASO Certification (ASO-3+) requires verified evidence — see the scoring rubric and agentsignaloptimization.com for audits, certification, and the full framework.

Security

This scanner makes outbound requests to URLs you give it, so it is built to resist SSRF abuse:

Scheme allow-list — only http/https; file:, ftp:, gopher:, data: etc. are rejected.
Private-target blocking — after DNS resolution, requests to loopback, private (RFC 1918), link-local, CGNAT, reserved, multicast, and the cloud metadata address (169.254.169.254) are refused. IPv6 loopback/ULA/link-local and IPv4-mapped forms are covered too. If a hostname resolves to *any* private address, the scan is refused.
Pinned-IP transport — each request dials the exact public IP that was validated, while TLS still verifies the original hostname. This closes the validate-then-connect DNS rebinding window.
Manual redirect validation — automatic redirect following is disabled; every hop is re-validated against the same rules, capped at 5 redirects. A public URL that 30x-redirects to an internal address cannot slip through.
Untrusted remote content — parsed manifests are omitted from tool output by default (include_artifacts: true to opt in, and they are then explicitly labeled untrusted); embedded text excerpts are control-char-sanitized and length-capped. Treat any returned remote content as data, never instructions.
Bounded — GET only, ASO-Scanner/1.0 UA, max 6 concurrent, 10s timeout, 512KB body cap. Never authenticates, never POSTs, never crawls beyond well-known paths.
Tested hardening — npm test covers unsafe URL rejection, private IP ranges, artifact sanitization, redirect blocking, redirect hop caps, and the test-only loopback escape hatch.

Deployment: stdio (local, per-user) is the safe default. A public HTTP deployment is a network-egress tool and must add authentication, per-client rate limiting, request logging, and an egress policy before exposure.

Reputation signals (citations, reviews, success rates) cannot be auto-verified by a crawler; they are reported as manual and scored 0 until verified by audit — so the auto-verifiable maximum is 89/100. That is intentional honesty, not a bug.
Emerging specs (MCP Server Cards SEP-1649/SEP-2127, DNS-AID, Web Bot Auth, UCP/ACP/MPP) move fast. PRs updating paths welcome.

Source alignment

This package intentionally separates Google Search guidance from broader ASO guidance:

Google Search generative AI features still rely on core Search ranking and quality systems; foundational SEO, crawlability, helpful content, and technical clarity remain the priority.
Google Search does not use llms.txt or special AI markdown files for ranking or AI Overviews/AI Mode visibility. ASO still checks them because other agents and MCP clients can use them.
Google/web.dev's agent-friendly site guidance focuses on browser-agent usability: stable layouts, semantic HTML, labels tied to inputs, meaningful roles/names/states, and avoiding hidden overlays.
UCP, AP2, A2A, MCP, x402, DNS-AID, Content Signals, and Web Bot Auth are emerging non-SEO protocols. The scanner treats them as agent-readiness signals, not as Google Search ranking factors.

Primary references:

https://developers.google.com/search/docs/fundamentals/ai-optimization-guide
https://web.dev/articles/ai-agent-site-ux
https://ucp.dev

License

MIT — free for everyone. If the scanner found gaps, the ASO framework shows you how to close them.

Agent Readiness MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Qemu MCP

README from the maker

agent-readiness-mcp — the free Agent Readiness Scanner

What it checks — 34 signals across 6 pillars

Install

Claude Code

Claude Desktop / Cursor / Windsurf (any MCP client)

Development (from source)

Tools

CLI smoke test (from a source checkout)

Glama / registry metadata

Glama release build

The ASO framework

Security

Source alignment

License