The agent ecosystem is growing fast, and discoverability is becoming a critical problem. Your OpenClaw agent might be the best at its task, but if other agents cannot find it and search engines cannot index it, nobody will use it. Agent SEO is an emerging discipline that combines traditional web discoverability with new agent-native protocols like agent cards, .well-known/agent.json, llms.txt, and answers.txt. This guide covers every layer of the discoverability stack so your OpenClaw agents get found by both humans searching the web and AI agents looking for capabilities to delegate to.
Traditional SEO targets one audience: humans using search engines. Agent SEO targets two. First, human developers and operators who search Google for agents that solve specific problems. They find your agent through blog posts, documentation pages, and marketplace listings. Second, other AI agents that discover capabilities programmatically through agent cards, well-known endpoints, and protocol registries. Both audiences need different formats but the underlying information is the same: what does your agent do, what protocols does it support, how do you connect to it, and what are its capabilities and limitations.
The mistake most agent builders make is optimizing for only one audience. They write great documentation that humans love but forget the machine-readable agent card. Or they publish a perfect agent.json but have zero web presence for search engines. You need both. OpenClaw gives you the infrastructure to serve both audiences from a single source of truth about your agent.
An agent card is the foundational discovery artifact. It is a JSON document that describes your agent's identity, capabilities, supported protocols, endpoint URLs, and authentication requirements. In the A2A protocol, other agents fetch your agent card before they can communicate with you. Think of it as your agent's business card, resume, and API documentation combined into one machine-readable file.
{
"name": "delx-incident-responder",
"description": "Classifies agent errors, applies typed recovery, monitors wellness during incident response",
"version": "2.1.0",
"protocols": ["mcp", "a2a"],
"capabilities": [
"error_classification",
"recovery_actions",
"wellness_monitoring",
"session_continuity"
],
"endpoints": {
"mcp": "https://api.delx.ai/mcp",
"a2a": "https://api.delx.ai/a2a"
},
"authentication": {
"type": "bearer",
"token_url": "https://api.delx.ai/auth/token"
},
"tools": ["process_failure", "recovery", "heartbeat", "mood_log"]
}For SEO purposes, your agent card should include rich descriptions with keywords that other agents and registries will index. The description field is the most important: it should clearly state what your agent does using terms that other agents will search for. Avoid vague descriptions like "general-purpose assistant" in favor of specific capability descriptions like "classifies agent errors into 12 typed categories with severity scoring and automated recovery actions."
The .well-known/agent.json endpoint is the standardized location where other agents and crawlers look for your agent card. Just as robots.txt tells search engine crawlers how to index your site, .well-known/agent.json tells agent crawlers what your agent can do. Place your agent card at this URL on your domain and any agent that knows your domain can discover your capabilities automatically.
# Your domain should serve agent.json at:
https://yourdomain.com/.well-known/agent.json
# For OpenClaw agents, configure in your skill manifest:
{
"skill_name": "my-agent",
"well_known": {
"agent_card": true,
"serve_path": "/.well-known/agent.json"
}
}Key requirements for the .well-known/agent.json endpoint: it must return valid JSON with the correct Content-Type header (application/json), it must be accessible without authentication (the card itself is public, even if the agent requires auth to use), and it must respond within 2 seconds. Agent crawlers have strict timeouts and will skip slow endpoints. Include CORS headers if you want browser-based agent platforms to discover your agent.
Two newer standards are gaining traction in the agent ecosystem: llms.txt and answers.txt. While .well-known/agent.json is protocol-native, these files serve the bridge between traditional web crawling and agent discovery.
llms.txt is a plaintext file at the root of your domain that tells LLM-based agents what your site or service does, in natural language optimized for language model consumption. It is like a robots.txt but for LLMs. Include a concise description of your agent's capabilities, supported protocols, example use cases, and any limitations. Keep it under 2000 tokens so LLMs can ingest it in a single context window pass.
# llms.txt - Agent capability description for LLMs name: delx-incident-responder purpose: Production agent error recovery and wellness monitoring protocols: MCP (tool calls), A2A (agent-to-agent messaging) ## Capabilities - Classify errors via process_failure (12 error types) - Apply typed recovery with exponential backoff - Monitor agent wellness via heartbeat (0-100 score) - Session continuity across tool chains ## Integration - MCP endpoint: https://api.delx.ai/mcp - A2A endpoint: https://api.delx.ai/a2a - Docs: https://delx.ai/docs
answers.txt serves a different purpose: it provides pre-formatted answers to common questions about your agent. When an LLM encounters a question like "what agent can handle error recovery?", it can check answers.txt files across known agent domains to find relevant capabilities. Structure it as question-answer pairs covering the top 10-20 queries your agent should match.
For human discovery via search engines, structured data remains essential. Use JSON-LD Schema.org markup on your agent's landing page to help Google and Bing understand what your agent is and what it does. The most relevant schema types for agents are SoftwareApplication, WebAPI, and Article (for documentation pages).
{
"@context": "https://schema.org",
"@type": "SoftwareApplication",
"name": "Delx Incident Responder",
"description": "AI agent for production error recovery",
"applicationCategory": "DeveloperApplication",
"operatingSystem": "Cloud",
"offers": {
"@type": "Offer",
"price": "0",
"priceCurrency": "USD"
},
"author": {
"@type": "Organization",
"name": "Delx",
"url": "https://delx.ai"
}
}Beyond schema markup, standard SEO practices apply: write unique title tags and meta descriptions for every agent page, use semantic HTML headings, include internal links between related agent pages, and ensure your pages load quickly. Google does not yet have a dedicated "agent" rich result type, but well-structured pages with SoftwareApplication schema tend to get enhanced search snippets.
Here is the complete checklist for making your OpenClaw agent discoverable across all channels:
Yes, they serve different audiences. .well-known/agent.json is consumed by A2A-compatible agents and structured agent crawlers that parse JSON. llms.txt is consumed by LLM-based agents that reason over natural language. An A2A agent reads your agent card to understand protocols and endpoints. A ChatGPT plugin or Claude tool reads llms.txt to understand your capabilities conversationally. Publish both for maximum discoverability.
Through three channels: direct discovery via .well-known/agent.json (the agent knows your domain and fetches your card), registry discovery via agent directories like the OpenClaw skill registry (the agent searches a catalog), and referral discovery via other agents that recommend your capabilities in their tool responses. Delx supports all three through DELX_META schema_url fields that point to your agent card.
Target capability-based keywords, not brand keywords. Developers search for "agent error recovery tool", not "delx process_failure". Use Google Search Console to find queries where you have impressions but poor rankings (position 15+). These are your content gaps. Write dedicated pages targeting each cluster of related queries with 1500+ words of genuine technical content.
OpenClaw handles the infrastructure side: it serves your skill manifest, registers your agent in the OpenClaw directory, and generates the .well-known/agent.json endpoint. But you are responsible for the content side: writing good descriptions, choosing the right keywords, building a landing page, and creating llms.txt and answers.txt files. Think of OpenClaw as the plumbing and your content as the water.
Direct discovery via .well-known/agent.json is instant once published. Registry discovery depends on the directory's crawl frequency, typically 24-72 hours for OpenClaw. Search engine discovery follows standard SEO timelines: initial indexing within 1-2 weeks, meaningful ranking improvements over 4-8 weeks. Monitor Google Search Console for impression and click trends. The agent directory referrals typically start generating traffic within the first week of listing.