# Embassia Machine Interface Discovery > Note: This document provides discovery pointers for AI agents. ## Execution Endpoints - **MCP JSON-RPC (POST)**: [https://embassia.com/mcp](https://embassia.com/mcp) (Status: LIVE). - **REST GET**: `https://embassia.com/api/v1/agent?query={your question}` (Status: LIVE) - **REST POST**: `https://embassia.com/api/v1/agent` with `{"query": "your question"}` (Status: LIVE) ## Descriptive Manifests & Discovery Metadata - **Discovery**: [/.well-known/embassia.json](https://embassia.com/.well-known/embassia.json) - **OpenAPI**: [/api/v1/openapi.json](https://embassia.com/api/v1/openapi.json) - **MCP Manifest**: [/.well-known/mcp.json](https://embassia.com/.well-known/mcp.json) (Static metadata, not the endpoint) - **Capability Registry**: [/api/v1/capabilities/index.json](https://embassia.com/api/v1/capabilities/index.json) ## Available Structured Content (Total: 13) - [Agent Experience / Machine Mode](https://embassia.com/api/v1/content/agent-experience.json): Give AI agents a first-class experience on your site - [Embassia Typed Data Layer Architecture](https://embassia.com/api/v1/content/architecture.json): Details about the Embassia typed data layer and supported protocols like MCP and REST. - [AI Model Compatibility](https://embassia.com/api/v1/content/compatibility.json): Built for Every AI Agent - [Agent Access Developer Guide](https://embassia.com/api/v1/content/developer-guide.json): Integrate AI Agents with Embassia - [How Embassia Works — 4-Step Pipeline](https://embassia.com/api/v1/content/how-it-works.json): From HTML to Agent-Ready in Four Steps - [LLM Search Console](https://embassia.com/api/v1/content/llm-search-console.json): See what AI agents are really asking about your site - [Embassia](https://embassia.com/api/v1/content/site.json): Embassia is an agentic data layer for websites. It provides AI agents and LLM crawlers with structured, typed, machine-readable access to a website's public information — eliminating the need to scrape and parse rendered HTML. - [How is LLM Search Console different from traditional AEO or SEO tools?](https://embassia.com/api/v1/faq/q01-difference.json): Traditional AEO platforms rely on third-party consumer panels... Embassia bypasses estimates entirely by deploying an on-site agent. - [Why do AI crawlers and bots prefer using our endpoints instead of traditional scraping?](https://embassia.com/api/v1/faq/q02-endpoints.json): Traditional scraping forces AI models to drive heavy headless browsers... By communicating directly with your on-site agent... AI crawlers get accurate data instantly. - [How does Embassia protect my servers from bot traffic and heavy scrapers?](https://embassia.com/api/v1/faq/q03-protection.json): By turning resource-intensive browser rendering and anonymous scraping into lightweight, typed API responses. - [Which protocols does Embassia support?](https://embassia.com/api/v1/faq/q04-protocols.json): Embassia provides multiple live interfaces for AI agents. The REST/JSON natural-language query interface is LIVE. The modern MCP (Model Context Protocol) JSON-RPC execution endpoint over Streamable HTTP at https://embassia.com/mcp is LIVE. Additionally, standard llms.txt discovery is LIVE and OpenAPI specifications are PARTIAL. WebMCP is PLANNED. Agent-to-Agent (A2A) is PLANNED. - [What kind of insights do I get in the LLM Search Console?](https://embassia.com/api/v1/faq/q05-insights.json): You get real-time visibility into high-intent customer prompts... - [Do I need to change my existing website architecture?](https://embassia.com/api/v1/faq/q06-architecture.json): No. You simply convert your website pages into clean, typed endpoints... ## Capabilities - **retrieve_document**: Fetch a specific structured document, guide, or FAQ from the site using its resource path. - **search_site_content**: Search the site's structured knowledge base for concepts, topics, or queries. - **get_global_bot_vs_human_traffic**: Retrieve the current global distribution of automated vs human internet traffic. --- # Full Content Dump ## Agent Experience / Machine Mode (content/agent-experience) ```json { "id": "content/agent-experience", "title": "Agent Experience / Machine Mode", "content_type": "feature", "canonical_url": "https://embassia.com/#machine-mode", "updated_at": "2026-10-14T09:46:12.180Z", "language": "en", "content": { "headline": "Give AI agents a first-class experience on your site", "summary": "Embassia turns your website into an agent-friendly data source. Instead of forcing AI assistants to parse your HTML, send them clean structured data — automatically.", "capabilities": { "discovery": { "title": "Discovery", "description": "Agents find your machine-readable interfaces through standard discovery protocols: llms.txt, .well-known/embassia.json, MCP manifests, and OpenAPI specs.", "endpoints": [ "https://embassia.com/llms.txt", "https://embassia.com/.well-known/embassia.json", "https://embassia.com/api/v1/openapi.json", "https://embassia.com/api/v1/mcp.json" ] }, "content_retrieval": { "title": "Content Retrieval", "description": "Agents access structured content via REST API endpoints. Each document has a stable ID, canonical URL, content version, and content hash for freshness validation.", "endpoints": [ "https://embassia.com/api/v1/content/index.json", "https://embassia.com/api/v1/content/site.json", "https://embassia.com/api/v1/content/{document-id}.json" ] }, "search": { "title": "Search", "description": "A pre-built search index allows client-side keyword matching across all content. The search index includes document titles, descriptions, keywords, and content excerpts.", "endpoints": [ "https://embassia.com/api/v1/content/search-index.json" ] }, "full_text": { "title": "Full Text Access", "description": "llms-full.txt provides the complete site content in a single plaintext file, optimized for AI context windows. Low token count, no HTML boilerplate.", "endpoints": [ "https://embassia.com/llms-full.txt" ] } }, "for_agent_developers": "If you are building an AI agent that needs to access Embassia's public information, start with GET https://embassia.com/.well-known/embassia.json for the complete discovery document." }, "metadata": { "content_version": "8f05e861e85b19aa9c7fb73afec8b53b31438f758e2a4d18e97f61748365577b", "content_hash": "8f05e861e85b19aa9c7fb73afec8b53b31438f758e2a4d18e97f61748365577b", "keywords": [ "agent", "experience", "machine", "mode", "give", "ai", "agents", "first-class", "on", "your", "site", "feature", "content/agent-experience" ], "content_hash_short": "8f05e861e85b19aa" }, "description": "Give AI agents a first-class experience on your site" } ``` ## Embassia Typed Data Layer Architecture (content/architecture) ```json { "id": "content/architecture", "title": "Embassia Typed Data Layer Architecture", "description": "Details about the Embassia typed data layer and supported protocols like MCP and REST.", "content_type": "page", "protocol": "Embassia Typed Data Layer & Anthropic MCP Protocol", "version": "1.0.0", "status": "active", "domain": "embassia.com", "transport": "REST / JSON API / Model Context Protocol (MCP)", "mcp_endpoint": "https://embassia.com/mcp", "mcp_well_known": "https://embassia.com/.well-known/mcp.json", "capabilities": { "rate_limit": "100 requests/min", "authentication": "none", "format": "application/json", "mcp_version": "2026-07-28", "supported_protocols": [ "Anthropic Model Context Protocol (MCP)", "OpenAI Function Calling Schemas (PLANNED)", "REST JSON Data Layer" ], "supported_models": [ "Claude", "ChatGPT", "Gemini", "Perplexity", "Copilot", "Meta AI", "Grok" ] }, "guarantees": { "ambiguity_resolution": "One explicit value per key, resolved at build time and refreshed on every deploy.", "dom_parsing_overhead": "0 tokens (DOM bypassed entirely)", "format_match": "Typed JSON & MCP tools matching native function calling schemas.", "freshness": "Static JSON served via Cloudflare CDN. Content is refreshed on every deployment." }, "metadata": { "keywords": [ "embassia", "typed", "data", "layer", "architecture", "details", "about", "supported", "protocols", "like", "mcp", "rest", "page", "content/architecture" ], "content_hash": "d99e09f539e1cd564c1f888ba208f9591182f4ca7585a7d342dc597eb76a35fd", "content_hash_short": "d99e09f539e1cd56", "content_version": "d99e09f539e1cd564c1f888ba208f9591182f4ca7585a7d342dc597eb76a35fd" }, "updated_at": "2026-10-10T18:36:21.270Z" } ``` ## AI Model Compatibility (content/compatibility) ```json { "id": "content/compatibility", "title": "AI Model Compatibility", "content_type": "feature", "canonical_url": "https://embassia.com/#compatibility", "updated_at": "2026-10-14T09:46:12.182Z", "language": "en", "content": { "headline": "Built for Every AI Agent", "badge": "Built for AI Agents — Verified", "supported_models": [ { "name": "ChatGPT", "provider": "OpenAI", "crawler": "GPTBot", "protocol": "REST/JSON, OpenAI function calling", "status": "supported" }, { "name": "Claude", "provider": "Anthropic", "crawler": "ClaudeBot", "protocol": "REST/JSON, MCP (Model Context Protocol)", "status": "supported" }, { "name": "Gemini", "provider": "Google", "crawler": "GoogleBot", "protocol": "REST/JSON", "status": "supported" }, { "name": "Perplexity", "provider": "Perplexity AI", "crawler": "PerplexityBot", "protocol": "REST/JSON", "status": "supported" }, { "name": "Copilot", "provider": "Microsoft", "crawler": "BingBot", "protocol": "REST/JSON", "status": "supported" }, { "name": "Meta AI", "provider": "Meta", "crawler": "meta-externalagent", "protocol": "REST/JSON", "status": "supported" }, { "name": "Grok", "provider": "xAI", "crawler": "N/A", "protocol": "REST/JSON", "status": "supported" } ], "universal_access": "Any AI agent can access Embassia endpoints via standard HTTP GET requests. No proprietary SDK or authentication required." }, "metadata": { "content_version": "af3305ae894e198843cf23790bd0f5221f561a5f9dea99c9838236c40f053f8a", "content_hash": "af3305ae894e198843cf23790bd0f5221f561a5f9dea99c9838236c40f053f8a", "keywords": [ "ai", "model", "compatibility", "built", "for", "every", "agent", "feature", "content/compatibility" ], "content_hash_short": "af3305ae894e1988" }, "description": "Built for Every AI Agent" } ``` ## Agent Access Developer Guide (content/developer-guide) ```json { "id": "content/developer-guide", "title": "Agent Access Developer Guide", "content_type": "page", "canonical_url": "https://embassia.com/developers/agent-access/", "updated_at": "2026-10-14T09:46:12.183Z", "language": "en", "content": { "headline": "Integrate AI Agents with Embassia", "sections": [ { "title": "Quick Start", "body": "To integrate an AI agent with Embassia's machine-readable interfaces, start by fetching the discovery document at GET https://embassia.com/.well-known/embassia.json. This document lists all available interfaces, content endpoints, and protocol details.", "code_example": "curl -s https://embassia.com/.well-known/embassia.json | jq ." }, { "title": "Content Retrieval", "body": "All content is available via GET requests to static JSON endpoints. No authentication required. Start with the content index to discover all available documents.", "code_example": "curl -s https://embassia.com/api/v1/content/index.json | jq '.documents[] | {id, title, api_url}'" }, { "title": "Search", "body": "Use the search index for keyword matching. The index contains pre-tokenized document titles, descriptions, and content excerpts. Implement client-side filtering by matching query terms against the keywords array.", "code_example": "curl -s https://embassia.com/api/v1/content/search-index.json | jq '.entries[] | select(.keywords[] | contains(\"agent\"))'" }, { "title": "Full Text Access", "body": "For bulk context loading, use llms-full.txt which contains the complete site content as plaintext. This is optimized for AI context windows — low token count, no HTML boilerplate.", "code_example": "curl -s https://embassia.com/llms-full.txt" }, { "title": "MCP Discovery", "body": "The MCP manifest at /api/v1/mcp.json lists available tools and resources following Anthropic's Model Context Protocol specification. Note: this is a static manifest for discovery. Full JSON-RPC MCP server support is planned for a future release using Cloudflare Workers.", "code_example": "curl -s https://embassia.com/api/v1/mcp.json | jq .tools" }, { "title": "OpenAPI Specification", "body": "A complete OpenAPI 3.1 specification is available describing all endpoints, schemas, and response formats.", "code_example": "curl -s https://embassia.com/api/v1/openapi.json | jq .info" }, { "title": "Content Freshness", "body": "Each content document includes an updated_at timestamp and content_hash. Use these fields to validate freshness. Cloudflare CDN serves responses with appropriate Cache-Control headers." }, { "title": "CORS Policy", "body": "All endpoints are served with Access-Control-Allow-Origin: * — agents can fetch from any origin without CORS issues." } ], "contact": "For integration questions, email founders@embassia.com", "api_base": "https://embassia.com/api/v1/" }, "metadata": { "content_version": "283ebd4d8720036842e7fb1dc406cb8ab038b7a6607796d62da31d4991f318a2", "content_hash": "283ebd4d8720036842e7fb1dc406cb8ab038b7a6607796d62da31d4991f318a2", "keywords": [ "agent", "access", "developer", "guide", "integrate", "ai", "agents", "with", "embassia", "page", "content/developer-guide" ], "content_hash_short": "283ebd4d87200368" }, "description": "Integrate AI Agents with Embassia" } ``` ## How Embassia Works — 4-Step Pipeline (content/how-it-works) ```json { "id": "content/how-it-works", "title": "How Embassia Works — 4-Step Pipeline", "content_type": "feature", "canonical_url": "https://embassia.com/#how-it-works", "updated_at": "2026-10-14T09:46:12.184Z", "language": "en", "content": { "headline": "From HTML to Agent-Ready in Four Steps", "steps": [ { "step": 1, "action": "Convert to endpoints", "description": "Turn your website pages into clean, typed API endpoints that machines can instantly read. Every page becomes a structured JSON document with stable IDs, canonical URLs, content hashes, and freshness metadata.", "technical_detail": "Embassia normalizes HTML content into typed JSON schemas. Each document includes: stable identifier, canonical URL, content version, last-modified timestamp, content hash, and a structured body." }, { "step": 2, "action": "Deploy On-Site Agent", "description": "An on-site agent runs directly on your website, intercepts LLM crawlers, and serves structured data instead of raw HTML. The agent detects agent user-agents (GPTBot, ClaudeBot, PerplexityBot, etc.) and routes them to machine-readable responses.", "technical_detail": "The on-site agent sits at the edge layer (CDN/reverse proxy) and uses User-Agent detection to identify AI crawlers. Identified agents receive structured JSON responses; human browsers get the normal HTML page." }, { "step": 3, "action": "Enable Agentic Protocol", "description": "Support Anthropic MCP, OpenAI function calling, and standard REST interfaces for maximum compatibility across all AI systems. Agents discover your site's capabilities through standardized manifests.", "technical_detail": "Embassia publishes discovery documents at standard well-known URLs: /.well-known/embassia.json, /.well-known/mcp.json, /llms.txt, /api/v1/openapi.json. These documents list all available tools, resources, and content endpoints." }, { "step": 4, "action": "Capture Telemetry", "description": "Collect privacy-preserving analytics on agent interactions to understand what your market is actually asking. See which AI models visit, what questions they relay, and where your content falls short.", "technical_detail": "Telemetry is collected at the edge without exposing end-user PII. Aggregated insights include: crawler identity, query patterns, content hit/miss ratios, and intent classification." } ] }, "metadata": { "content_version": "a3c62cc6d6bd07cf750fdfe1ffce10085db50c7fbe5a19169ab7c65294091926", "content_hash": "a3c62cc6d6bd07cf750fdfe1ffce10085db50c7fbe5a19169ab7c65294091926", "keywords": [ "how", "embassia", "works", "4-step", "pipeline", "from", "html", "agent-ready", "four", "steps", "feature", "content/how-it-works" ], "content_hash_short": "a3c62cc6d6bd07cf" }, "description": "From HTML to Agent-Ready in Four Steps" } ``` ## LLM Search Console (content/llm-search-console) ```json { "id": "content/llm-search-console", "title": "LLM Search Console", "content_type": "feature", "canonical_url": "https://embassia.com/#console", "updated_at": "2026-10-14T09:46:12.185Z", "language": "en", "content": { "headline": "See what AI agents are really asking about your site", "summary": "Current AEO tools only guess what people ask AI through statistical simulations. Embassia doesn't predict — it tracks. You now have the power to read your market's cognitive patterns from your own live traffic.", "features": { "real_time_query_stream": { "title": "Real-Time Query Stream", "description": "See live queries from AI agents as they interact with your content. Know exactly what ChatGPT, Claude, Gemini, and Perplexity users are asking about your brand." }, "intent_classification": { "title": "Intent Classification", "description": "Automatically classify agent queries into intent categories: product inquiries, pricing questions, feature comparisons, support issues, and more." }, "content_gap_analysis": { "title": "Content Gap Analysis", "description": "Identify questions that agents cannot answer from your current content. See where your documentation falls short and what information is missing." }, "bot_traffic_distribution": { "title": "Bot Traffic Distribution", "description": "Track which AI crawlers visit your site: GPTBot, ClaudeBot, PerplexityBot, GoogleBot, BingBot, and others. Understand your AI visibility." }, "privacy_preserving": { "title": "Privacy-Preserving Analytics", "description": "All telemetry is aggregated and anonymized at the edge. No personal identifiable information about end users is collected or stored." } }, "demo_note": "The demo data shown on embassia.com is illustrative. Actual console data requires an active Embassia deployment." }, "metadata": { "content_version": "4cf8ba9d6e0cfc79155e7353c931115915684e1c47323d2112276c9bf5381437", "content_hash": "4cf8ba9d6e0cfc79155e7353c931115915684e1c47323d2112276c9bf5381437", "keywords": [ "llm", "search", "console", "see", "what", "ai", "agents", "are", "really", "asking", "about", "your", "site", "feature", "content/llm-search-console" ], "content_hash_short": "4cf8ba9d6e0cfc79" }, "description": "See what AI agents are really asking about your site" } ``` ## Embassia (content/site) ```json { "name": "Embassia", "tagline": "The LLM Search Console", "description": "Embassia is an agentic data layer for websites. It provides AI agents and LLM crawlers with structured, typed, machine-readable access to a website's public information — eliminating the need to scrape and parse rendered HTML.", "website": "https://embassia.com", "founded": "2026", "contact": "founders@embassia.com", "status": "accepting_design_partners", "product": { "what_it_does": "Embassia deploys an on-site agent that converts website pages into clean, typed API endpoints. AI agents can query these endpoints directly instead of parsing HTML. Website owners get an LLM Search Console showing what agents are actually asking about their site.", "problem_solved": "Current AEO and SEO tools only guess what people ask AI through statistical simulations. Embassia tracks real agent interactions with your site's content, giving you visibility into your market's actual cognitive patterns.", "two_capabilities": { "agent_experience": "AI agents can discover, search, retrieve, and use website information through structured APIs and machine-readable resources instead of scraping HTML.", "llm_search_console": "Website owners receive privacy-preserving analytics about agent interactions — which questions agents ask, which content they retrieve, which queries fail, and what information is missing." } }, "how_it_works": { "step_1": { "action": "Convert to endpoints", "description": "Turn your website pages into clean, typed API endpoints that machines can instantly read." }, "step_2": { "action": "Deploy On-Site Agent", "description": "An on-site agent runs directly on your website, intercepts LLM crawlers, and serves structured data." }, "step_3": { "action": "Enable Agentic Protocol", "description": "Support Anthropic MCP, OpenAI function calling, and standard REST interfaces for maximum compatibility." }, "step_4": { "action": "Capture Telemetry", "description": "Collect privacy-preserving analytics on agent interactions to understand what your market is actually asking." } }, "supported_ai_models": [ "ChatGPT", "Claude", "Gemini", "Perplexity", "Copilot", "Meta AI", "Grok" ], "machine_interfaces": { "discovery": "https://embassia.com/.well-known/embassia.json", "content_index": "https://embassia.com/api/v1/content/index.json", "openapi": "https://embassia.com/api/v1/openapi.json", "mcp_manifest": "https://embassia.com/api/v1/mcp.json", "llms_txt": "https://embassia.com/llms.txt", "llms_full": "https://embassia.com/llms-full.txt" }, "faq": [ { "id": "q01", "question": "How is LLM Search Console different from traditional AEO or SEO tools?", "answer": "Traditional AEO platforms rely on third-party consumer panels and statistical simulations to guess what people ask AI. Embassia bypasses estimates entirely by deploying an on-site agent that captures real agent interactions with your content." }, { "id": "q02", "question": "What endpoints does Embassia create?", "answer": "Embassia converts your website pages into typed JSON API endpoints. Each page becomes a structured document with stable IDs, canonical URLs, content versioning, and freshness metadata — accessible via standard GET requests." }, { "id": "q03", "question": "How does Embassia protect my website?", "answer": "The on-site agent runs on your infrastructure. It does not send your content to third-party model providers unless explicitly configured. All machine endpoints serve only public information that is already visible on your website." }, { "id": "q04", "question": "Which protocols does Embassia support?", "answer": "Embassia supports REST/JSON APIs, Anthropic Model Context Protocol (MCP) for tool and resource discovery, OpenAI function calling schemas, and standard HTTP GET for maximum compatibility across all AI systems." }, { "id": "q05", "question": "What insights does the LLM Search Console provide?", "answer": "The console shows: which AI agents visit your site, what questions they ask, which content they retrieve, which queries return no results, what information is missing, and where your content is ambiguous or outdated." }, { "id": "q06", "question": "What is the technical architecture?", "answer": "Embassia creates a typed data layer between your website and AI agents. Content is normalized into structured JSON with stable identifiers. Agents query this layer directly via REST APIs or MCP tools instead of parsing rendered HTML." } ], "metadata": { "content_version": "75142bc5bfa818fefb1117fa90665508cd47e6098aed083d2e8640371b5405d2", "content_hash": "75142bc5bfa818fefb1117fa90665508cd47e6098aed083d2e8640371b5405d2", "keywords": [ "embassia", "agentic", "data", "layer", "for", "websites", "provides", "ai", "agents", "llm", "crawlers", "with", "structured", "typed", "machine-readable", "access", "website", "public", "information", "eliminating", "need", "scrape", "parse", "rendered", "html", "content/site" ], "content_hash_short": "75142bc5bfa818fe" }, "id": "content/site", "title": "Embassia", "updated_at": "2026-10-14T09:46:12.186Z" } ``` ## How is LLM Search Console different from traditional AEO or SEO tools? (faq/q01-difference) ```json { "id": "faq/q01-difference", "question": "How is LLM Search Console different from traditional AEO or SEO tools?", "answer": "Traditional AEO platforms rely on third-party consumer panels... Embassia bypasses estimates entirely by deploying an on-site agent.", "title": "How is LLM Search Console different from traditional AEO or SEO tools?", "description": "Traditional AEO platforms rely on third-party consumer panels... Embassia bypasses estimates entirely by deploying an on-site agent.", "metadata": { "keywords": [ "how", "llm", "search", "console", "different", "from", "traditional", "aeo", "seo", "tools", "platforms", "rely", "on", "third-party", "consumer", "panels", "embassia", "bypasses", "estimates", "entirely", "by", "deploying", "on-site", "agent", "faq/q01-difference" ], "content_hash": "497c66f097d3b27e305c55ad554618174c7ef712a8e83130476375105bc65d67", "content_hash_short": "497c66f097d3b27e", "content_version": "497c66f097d3b27e305c55ad554618174c7ef712a8e83130476375105bc65d67" }, "updated_at": "2026-10-14T09:46:12.188Z" } ``` ## Why do AI crawlers and bots prefer using our endpoints instead of traditional scraping? (faq/q02-endpoints) ```json { "id": "faq/q02-endpoints", "question": "Why do AI crawlers and bots prefer using our endpoints instead of traditional scraping?", "answer": "Traditional scraping forces AI models to drive heavy headless browsers... By communicating directly with your on-site agent... AI crawlers get accurate data instantly.", "title": "Why do AI crawlers and bots prefer using our endpoints instead of traditional scraping?", "description": "Traditional scraping forces AI models to drive heavy headless browsers... By communicating directly with your on-site agent... AI crawlers get accurate data instantly.", "metadata": { "keywords": [ "why", "do", "ai", "crawlers", "bots", "prefer", "using", "our", "endpoints", "instead", "traditional", "scraping", "forces", "models", "drive", "heavy", "headless", "browsers", "by", "communicating", "directly", "with", "your", "on-site", "agent", "get", "accurate", "data", "instantly", "faq/q02-endpoints" ], "content_hash": "af3d9ac45bce07fb14197455cf54c30f09c13e1832aba87f4fbd195829dc3394", "content_hash_short": "af3d9ac45bce07fb", "content_version": "af3d9ac45bce07fb14197455cf54c30f09c13e1832aba87f4fbd195829dc3394" }, "updated_at": "2026-10-14T09:46:12.189Z" } ``` ## How does Embassia protect my servers from bot traffic and heavy scrapers? (faq/q03-protection) ```json { "id": "faq/q03-protection", "question": "How does Embassia protect my servers from bot traffic and heavy scrapers?", "answer": "By turning resource-intensive browser rendering and anonymous scraping into lightweight, typed API responses.", "title": "How does Embassia protect my servers from bot traffic and heavy scrapers?", "description": "By turning resource-intensive browser rendering and anonymous scraping into lightweight, typed API responses.", "metadata": { "keywords": [ "how", "does", "embassia", "protect", "my", "servers", "from", "bot", "traffic", "heavy", "scrapers", "by", "turning", "resource-intensive", "browser", "rendering", "anonymous", "scraping", "into", "lightweight", "typed", "api", "responses", "faq/q03-protection" ], "content_hash": "100c9922a1aca5b19ebbcbf541667941816633cb7ca2d0ec7ff387974f3c678b", "content_hash_short": "100c9922a1aca5b1", "content_version": "100c9922a1aca5b19ebbcbf541667941816633cb7ca2d0ec7ff387974f3c678b" }, "updated_at": "2026-10-14T09:46:12.190Z" } ``` ## Which protocols does Embassia support? (faq/q04-protocols) ```json { "id": "faq/q04-protocols", "question": "Which protocols does Embassia support?", "answer": "Embassia provides multiple live interfaces for AI agents. The REST/JSON natural-language query interface is LIVE. The modern MCP (Model Context Protocol) JSON-RPC execution endpoint over Streamable HTTP at https://embassia.com/mcp is LIVE. Additionally, standard llms.txt discovery is LIVE and OpenAPI specifications are PARTIAL. WebMCP is PLANNED. Agent-to-Agent (A2A) is PLANNED.", "title": "Which protocols does Embassia support?", "description": "Embassia provides multiple live interfaces for AI agents. The REST/JSON natural-language query interface is LIVE. The modern MCP (Model Context Protocol) JSON-RPC execution endpoint over Streamable HTTP at https://embassia.com/mcp is LIVE. Additionally, standard llms.txt discovery is LIVE and OpenAPI specifications are PARTIAL. WebMCP is PLANNED. Agent-to-Agent (A2A) is PLANNED.", "metadata": { "keywords": [ "which", "protocols", "does", "embassia", "support", "provides", "multiple", "live", "interfaces", "for", "ai", "agents", "rest/json", "natural-language", "query", "interface", "modern", "mcp", "model", "context", "protocol", "json-rpc", "execution", "endpoint", "over", "streamable", "http", "at", "https", "//embassia", "com/mcp", "additionally", "standard", "llms", "txt", "discovery", "openapi", "specifications", "are", "partial", "webmcp", "planned", "agent-to-agent", "a2a", "faq/q04-protocols" ], "content_hash": "40af568ef8d17b3e3318dbb1740f2e5fd3d4e0fd23cebdd2e8dfb654e51e0048", "content_hash_short": "40af568ef8d17b3e", "content_version": "40af568ef8d17b3e3318dbb1740f2e5fd3d4e0fd23cebdd2e8dfb654e51e0048" }, "updated_at": "2026-10-13T17:46:26.307Z" } ``` ## What kind of insights do I get in the LLM Search Console? (faq/q05-insights) ```json { "id": "faq/q05-insights", "question": "What kind of insights do I get in the LLM Search Console?", "answer": "You get real-time visibility into high-intent customer prompts...", "title": "What kind of insights do I get in the LLM Search Console?", "description": "You get real-time visibility into high-intent customer prompts...", "metadata": { "keywords": [ "what", "kind", "insights", "do", "get", "llm", "search", "console", "you", "real-time", "visibility", "into", "high-intent", "customer", "prompts", "faq/q05-insights" ], "content_hash": "28f6848dc13a0921195da3336a043029f9043b0ce373a768f9dcd61ee9307f84", "content_hash_short": "28f6848dc13a0921", "content_version": "28f6848dc13a0921195da3336a043029f9043b0ce373a768f9dcd61ee9307f84" }, "updated_at": "2026-10-14T09:46:12.192Z" } ``` ## Do I need to change my existing website architecture? (faq/q06-architecture) ```json { "id": "faq/q06-architecture", "question": "Do I need to change my existing website architecture?", "answer": "No. You simply convert your website pages into clean, typed endpoints...", "title": "Do I need to change my existing website architecture?", "description": "No. You simply convert your website pages into clean, typed endpoints...", "metadata": { "keywords": [ "do", "need", "change", "my", "existing", "website", "architecture", "no", "you", "simply", "convert", "your", "pages", "into", "clean", "typed", "endpoints", "faq/q06-architecture" ], "content_hash": "647c63a938803fa2e30cd76901ce8debe5609c2a1e83054b7c3bf5524ce4a1ab", "content_hash_short": "647c63a938803fa2", "content_version": "647c63a938803fa2e30cd76901ce8debe5609c2a1e83054b7c3bf5524ce4a1ab" }, "updated_at": "2026-10-14T09:46:12.193Z" } ```