Agents are likely to struggle

Server-side render your homepage so AI crawlers see meaningful content without JavaScript. Ensure an H1 and 500+ chars of text in raw HTML.

Allowlist known AI agent User-Agents (ChatGPT-User, ClaudeBot, Google-Extended, DeepSeekBot) in your WAF or bot-detection rules.

Replace meta-refresh and JavaScript-only redirects with real HTTP 301/302 redirects. Non-JS agents never execute location.href or wait for a meta refresh - they see only the stub page. Verify with curl -sI <url> - you should see a Location header, not a 200 with a near-empty body.

Serve your content pages without a login wall. Agents cannot complete auth flows while browsing - a 401/403 or a login-form page is invisible content. Keep public documentation public; if some content must stay gated, publish an ungated summary so agents can still represent it.

Publish an OpenAPI (Swagger) specification at /openapi.json or /api/openapi.yaml. This is how agents understand your API surface automatically.

On the responses that serve text/markdown via Accept negotiation, add Accept to the Vary header (Vary: Accept, Accept-Encoding). Without it, CDNs can serve the cached HTML variant to an agent asking for markdown (or vice versa), depending on which variant landed in cache first.

Verify that major agent User-Agents can reach the homepage. If your WAF or bot rules block them, remove or narrow the blocking rule. Add an allow rule only when your security setup denies them by default.

Implement OAuth 2.0 for API authentication. Publish your authorization server metadata at /.well-known/oauth-authorization-server.

Declare scoped API permissions where machines can read them: named OAuth scopes in your OpenAPI security schemes, or scopes_supported in RFC 9728 protected-resource metadata. Prose descriptions of roles help humans, but agents need the machine-readable declaration to request least-privilege access.

Return structured JSON error responses with error codes, messages, and resolution hints. Agents can't parse HTML error pages.

Make your developer resources (API docs, OpenAPI spec, auth docs, webhooks, MCP server) discoverable by name. Publish them at predictable URLs, list them in llms.txt, and include your product name in page titles and headings so search engines surface them for name-based queries.

Make sure a clean search for your brand name returns your own domain in the top results. If it does not, your brand may be too generic, conflict with a more established term, or not yet indexed. Strengthen brand-name search by claiming consistent NAP across listings, earning press mentions that link to the canonical domain, and avoiding redirect chains that mask the apex domain in search results.

Add a valid XML sitemap at /sitemap.xml listing all indexable URLs. Include lastmod dates and keep it under 50MB.

Add JSON-LD structured data to your homepage using the identity type that matches your site - SoftwareApplication for products, Organization or LocalBusiness for companies, Person for personal sites, Article for blogs - with name, description, url, and type-appropriate fields (offers, sameAs, author) so AI can parse your identity programmatically.

Publish API documentation at a discoverable URL (/docs, /api, /developers). Include authentication, endpoints, and example requests.

Tell agents when to reach for you: add a 'when to use this' section to your llms.txt (or a dedicated agent-instructions file) that names your best-fit use cases and how an agent should call you. Be specific about the jobs you are right for - generic marketing copy does not read as guidance.

Add all four signals to your homepage: , , , and . Agents use these for entity resolution and attribution.

Add Organization JSON-LD that includes both contactPoint (with email/phone and contactType) and address (PostalAddress). This lets AI verify your business legitimacy and answer contact queries.

Publish real /about, /contact, and /privacy pages with at least 500 characters of content each. These are the pages AI agents check to verify your business is legitimate before recommending you.

Keep each page's extracted text under ~100K characters (~25K tokens) so it fits an agent's context window without truncation. Split oversized reference pages into focused per-topic documents and link them from an index. Check a page with curl -s <url> | wc -c and remember agents read the extracted text, not the raw HTML.

Close every fenced code block (``` or ~~~) in your served markdown. CommonMark treats everything after an unclosed fence as code, so an agent parsing the document silently loses the rest of it. Count fence lines per file - the total must be even.

Create a developer portal at /developers with API keys, documentation, quickstart guides, and a sandbox environment.

Expose a public REST or GraphQL API. AI agents need programmatic access - not just a web UI - to integrate with your product.

Offer a free tier or trial, self-serve API key generation, and a sandbox environment. Agents can't fill out 'contact sales' forms.

Build an MCP (Model Context Protocol) server exposing your API as tools. Use Streamable HTTP transport for full score. This lets Claude, ChatGPT, and other AI agents call your product natively.

Publish an official CLI tool on npm, PyPI, or Homebrew. A CLI lets agents and developers script interactions with your product without building API integrations from scratch.

Make your API spec self-describing: a unique operationId and a description on every operation, typed parameters, and response schemas. For GraphQL, a fully typed schema with a documented cost or rate limit reads best.

Ensure API endpoints have unique operation IDs, typed schemas, and descriptions compatible with LLM function-calling formats.

Establish a Wikipedia article and a Wikidata entity for your brand, with the domain set as the official website (Wikidata property P856) and a corresponding external link on Wikipedia. Wikipedia is the largest single source of citations in ChatGPT and a primary input to Knowledge Graphs across Google, Bing, and LLM training data. Earn third-party press coverage first to satisfy notability, then draft the article with cited references rather than self-promotion.

Publish a JavaScript/TypeScript SDK package on npm so developers can integrate your API programmatically. In package.json set repository to your source repo and homepage to your product domain - these links are how agents confirm the package is your official SDK rather than a third-party tool with a similar name.

Publish agent skills on skills.sh so AI agents can discover your product's capabilities. Create a SKILL.md in your GitHub repo and register it with 'npx skills add'. See skills.sh/docs.

Serve your MCP server at /.well-known/mcp, publish a server-card.json at /.well-known/mcp/server-card.json, or reference it in llms.txt so agents can discover it automatically without manual URL input.

Create an llms.txt file at your domain root (/llms.txt) - the AI equivalent of robots.txt. Write at least 100 characters of real content: what your product is, what it does, and links to your key docs. Then verify it with curl https://yourdomain.com/llms.txt - you should see your text, not HTML. If your app returns its homepage for every URL (common with single-page apps), add a static file route so the raw text is served. A placeholder with just a heading earns no credit.

Format your llms.txt as a navigation index: start with a markdown heading, include markdown links to deeper resources, and keep it under 30,000 characters. If you have more to say, move long-form content into /llms-full.txt or per-section files (e.g. /docs/llms.txt, /api/llms.txt) and link to them from the main index.

Make every link your llms.txt declares resolve to real content. Verify each one with curl -L <url> - you should see the linked document, not your homepage. If your app returns the homepage shell for unknown paths (common with single-page apps), a 200 status is not proof: check the body. Fix or remove any dead link; agents that follow the index treat a broken link as a dead end.

Publish RFC 9728 protected-resource metadata at /.well-known/oauth-protected-resource. Include the resource field plus enough supporting metadata - your authorization servers, supported scopes, accepted bearer methods - that an agent can work out how to authenticate without first triggering a 401.

Publish RFC 9728 protected-resource metadata at /.well-known/oauth-protected-resource on your resource server (the host that actually serves the API, e.g. api.) with resource and authorization_servers. Publish RFC 8414 authorization-server metadata at /.well-known/oauth-authorization-server on the AS origin, and include the WorkOS auth.md agent_auth block with register_uri, identity_types_supported drawn from the spec enum (anonymous, identity_assertion - variants like verified_email or urn:ietf:params:oauth:token-type:id-jag belong inside identity_assertion.assertion_types_supported, not at the top level), and a sibling per-type block for each advertised type (anonymous.credential_types_supported; identity_assertion.assertion_types_supported + credential_types_supported) so agents can look up the request shape. Cross-link by listing the AS origin in PRM authorization_servers, and point agent_auth.skill back at your published /auth.md. Spec: https://workos.com/auth-md.

Publish official SDK packages across multiple language ecosystems (npm, PyPI, Go modules, RubyGems). Auto-generate them from your OpenAPI spec using tools like openapi-generator. For each package set the project URL or homepage to your product domain (package.json repository/homepage, PyPI Home-Page or project_urls, RubyGems homepage_uri) - this is how agents verify the package is your official SDK.

Implement Microsoft's NLWeb protocol by adding a POST /ask endpoint that accepts natural-language queries and returns JSON with _meta (response_type, version). See github.com/microsoft/NLWeb.