Agent Ready
Agent Ready scans any URL for AI-agent readability against the Vercel Agent Readability Spec, llmstxt.org, and agent-protocol manifests, returning scores and per-check fix guidance.
Scan sites (
scan_site) — run a fresh scan on a URL for Vercel score (0–100), llms.txt sub-score, and per-check findings withhowToFixhints; works keyless (3 scans/30 days, 25-page depth, synchronous) or up to 250 pages with a Pro key.Retrieve past scans (
get_scan) — fetch a prior scan by id (Pro key required; scan history is account-scoped).Ask in natural language (
ask) — NLWeb search over Agent Ready's methodology, check registry, specs, and content library, with optional corpus filtering and extractive summaries; public, no key.Validate structured data (
validate_structured_data) — check a page's or a pasted JSON-LD string against schema-lint and agent-coherence checks, returning a verdict with fix guidance; public, no key.Run guided workflows — the
scan,interpret_scan, andremediation_planprompts take you from URL → score → prioritised fix list (optionally focused on SEO or agents).
Scans any URL for AI agent readability against the Vercel Agent Readability Spec, providing per-check findings and remediation guidance.
This server has a remotely hosted endpoint. Open its connector listing for connection details, authentication requirements, and setup instructions:
agent-ready-mcp
MCP server for Agent Ready — scan any URL for AI agent readability against the Vercel Agent Readability Spec, the llmstxt.org standard, and agent-protocol manifests (MCP server cards, A2A, agents.json, agent-permissions.json, UCP, x402, NLWeb). 72 checks across four spec families — 40 site and page checks (15 site-wide + 25 per-page), 10 against llmstxt.org, and 22 against agent-protocol manifests — each with per-check fix guidance, plus a separate accessibility sub-score from 23 WCAG 2.2 / layout-stability checks.
Hosted at https://agent-ready.dev/api/v1/mcp (Streamable HTTP); this package is a thin stdio wrapper around the same REST endpoints, distributed via npm for local MCP clients (Claude Desktop, Claude Code, Cursor, VS Code, Windsurf).
Features
scan_site— fresh agent-readability scan on any URL. Works without a key on the anonymous free tier (3 scans/30 days per IP, 25-page depth, synchronous); with a Pro key it scans up to 250 pages, polling the hosted API up to 60s.get_scan— fetch a previously-run scan by id (Pro key required — scan history is account-scoped).ask— natural-language (NLWeb) search over Agent Ready's own methodology, checks, and specs. Public, no API key required; returns Schema.org-typed results.validate_structured_data— validate a page's (or a pasted) JSON-LD against Agent Ready's structured-data checks. Public, no API key required; paste mode needs no network, so an agent can check JSON-LD it just authored.Three discovery prompts —
scan,interpret_scan,remediation_plan. End-to-end workflows from URL → score → fix-it plan.SKILL.md— Claude Skill descriptor included underskills/agent-ready/for activation routing.
Related MCP server: mcp-seo-audit
Setup
No key is required to start: scan_site, ask, and validate_structured_data all work anonymously out of the box (scan_site on the free anonymous quota — 3 scans per 30 days per IP at 25-page depth). An Agent Ready Pro API key unlocks 50 scans/month, 250-page depth, scan history (get_scan), and weekly monitoring — sign up at agent-ready.dev and issue a key from the dashboard. The env block in the configs below is optional; omit it to run keyless.
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"agent-ready": {
"command": "npx",
"args": ["-y", "agent-ready-mcp@latest"],
"env": {
"AGENT_READY_API_KEY": "ar_live_..."
}
}
}
}Claude Code
claude mcp add agent-ready \
-e AGENT_READY_API_KEY=ar_live_... \
-- npx -y agent-ready-mcp@latestCursor / VS Code / Windsurf
.cursor/mcp.json, .vscode/mcp.json, or ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"agent-ready": {
"command": "npx",
"args": ["-y", "agent-ready-mcp@latest"],
"env": {
"AGENT_READY_API_KEY": "ar_live_..."
}
}
}
}Environment variables
Variable | Required | Default | Purpose |
| No | — | Pro Bearer token from the Agent Ready dashboard. Without it, |
| No |
| Override for self-hosted or staging deployments. |
| No |
| How long |
| No |
| Timeout for |
Tools
Tool | Inputs | Returns |
|
| Scan object: Vercel score 0–100, llms.txt sub-score 0–100, per-check findings with |
|
| Same scan object as |
|
| NLWeb |
| exactly one of | D-series structured-data result: |
Prompts
Prompt | Args | What it does |
|
| Fresh scan + high-level summary (score, rating, top 3–5 failures, next step). |
|
| Plain-English explanation of a previous scan's findings, grouped by category. |
|
| Prioritised fix-it doc with Now/Next/Later buckets and per-fix check ids. |
Example workflow
You: Use agent-ready to scan https://my-saas.com
Claude: [calls scan_site] Your site scored 78/100 (Good) on the Vercel Agent
Readability Spec. The top 3 fixes: …
You: Can you build me a remediation plan?
Claude: [calls remediation_plan with the scan id] Here's the prioritised list…Skill (Anthropic Claude Skills)
A SKILL.md lives at skills/agent-ready/SKILL.md inside the package. To use it in Claude Desktop / Claude Code, copy the skills/agent-ready/ directory into ~/.claude/skills/.
The skill describes when to activate (URL + readability-audit intent), which tool to pick, how to surface scan results without dumping raw JSON, and when to defer to other tools (general SEO, performance profiling, code editing).
How it works
This package is a thin stdio→HTTPS wrapper:
MCP client (stdio) ↔ agent-ready-mcp ↔ HTTPS ↔ agent-ready.dev/api/v1/scansAll scan execution, persistence, and Pro-tier quota enforcement happen on the hosted server. The npm package only translates between MCP JSON-RPC over stdio and the REST API.
If you'd rather use the hosted MCP server directly (Streamable HTTP transport, no local install), point your MCP client at https://agent-ready.dev/api/v1/mcp with Authorization: Bearer ar_live_....
Methodology
The 72 checks, their weights, and the score formula are documented at agent-ready.dev/methodology. Both manifest.json and server.json in this repo conform to the relevant registry schemas (Glama Marketplace v0.3 and MCP registry 2025-12-11 respectively).
Development
npm install
npm run build # → dist/mcp-server.mjs
npm test
npm run typecheckReleasing
See RELEASE.md. It is the source of truth for version surfaces, local checks, tag pushing, the GitHub Actions release workflow, and the manual post-release steps.
License
MIT — see LICENSE.
Available Tools
4 toolsaskAsk Agent Ready in natural languageARead-onlyIdempotentInspect
Natural-language search (NLWeb /ask) over Agent Ready's own content — scoring methodology, the check registry, the specs it validates, and the content library (explainers, comparisons, how-to guides, glossary). Public, no API key required. Returns Schema.org-typed result objects; optional itemType narrows to a corpus type and mode 'summarize' adds an extractive summary.
Inputs: q (required), mode, itemType. Returns: mode, site, _meta, error, +4 more
get_scanGet a previous scan by idARead-onlyIdempotentInspect
Fetches a completed or in-progress scan by its id. Requires a Pro API key — scan history is account-scoped. (Anonymous scan_site calls return their result inline, so keyless use never needs this tool.)
Inputs: id (required). Returns: id, url, status, message, +14 more
scan_siteScan a site for AI agent readabilityARead-onlyIdempotentInspect
Runs the agent-ready.dev scanner against a URL and returns structured results: Vercel score, llmstxt.org score, and per-check findings with remediation hints. Works without an API key on the anonymous free tier (3 scans/30 days per IP, 25-page depth, synchronous). With a Pro AGENT_READY_API_KEY it scans deeper (up to 250 pages) and may take up to ~60s; if the local poll deadline elapses, the tool returns the scan id and asks you to poll with get_scan.
Inputs: url (required), pageLimit. Returns: id, url, status, message, +14 more
validate_structured_dataValidate JSON-LD structured dataARead-onlyIdempotentInspect
Validates a page's (or a pasted) JSON-LD against Agent Ready's structured-data checks (schema lint + agent-coherence: freshness honesty, canonical/.md coherence, entity-name consistency, extraction signal) and returns a verdict with per-check fix guidance. Provide exactly one of url (fetch + validate) or jsonld (validate a string the agent just authored — no network needed). Public, no API key required. The one structured-data check the first-party validators (validator.schema.org, Rich Results Test) don't do.
Inputs: url, jsonld. Returns: url, mode, checks, summary
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: scanning a site, retrieving scan results, searching documentation, and validating structured data. No overlap or ambiguity.
All tool names follow a consistent verb_noun snake_case pattern. 'ask' is a concise verb, but fits as it is a direct action.
Four tools are well-scoped for Agent Ready's domain: scanning, result retrieval, knowledge base search, and validation. Not too few or too many.
The tool surface covers the core workflows: scan execution, result polling, documentation lookup, and structured data validation. No obvious gaps for the stated purpose.
Maintenance
Related MCP Connectors
Scan a public website for AI agent readiness: 60 checks, a score, and the evidence for each.
Scan any public website and see how ready it is for AI agents and AI search, explained plainly.
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