How Agent Discovery Works

AI agents do not discover products the same way people do.

A person can skim a homepage, click through a navigation menu, infer intent from visual hierarchy, and tolerate some ambiguity. An agent usually needs a cleaner path. It needs to know what the product is, what actions are available, which interfaces are meant for machines, how authentication works, and which sources are authoritative enough to cite or use.

That is the problem agent discovery solves.

For Context Repo, agent discovery is not a single file or a single endpoint. It is a layered public surface that combines llms.txt, well-known metadata, MCP discovery, OpenAPI, markdown content negotiation, an agent catalog, and feature-specific agent routes. Each layer answers a different question an AI system might ask before it can safely recommend, describe, or use the product.

Agent Discovery in Plain English

Agent discovery is the process of making a website legible to AI systems.

Traditional SEO asks, "Can a search engine find and rank this page?"

Agent discovery asks a broader set of questions:

• Can an AI agent find the product when it starts from a domain or brand name?
• Can it understand the product category, use cases, pricing, and constraints?
• Can it identify the official machine-readable documentation?
• Can it discover callable APIs, MCP tools, or app integrations?
• Can it understand which actions require authentication?
• Can it cite the right sources instead of guessing from scraped prose?

This is why a product can have good human-facing documentation but still be hard for agents to use. If the useful facts are buried in rendered UI, spread across pages without machine-readable summaries, or disconnected from executable contracts, the agent has to infer too much.

Context Repo is built around the opposite assumption: agents should be given explicit, public, structured paths into the product.

What Ora.ai Checks

Ora.ai is an external agent-readiness checker. Its public methodology describes a 100-point audit for how well AI agents can find, understand, authenticate with, integrate with, and use a product.

Ora describes its scanner as using automated checks against public URLs, with no login or self-reporting required. Its methodology groups the score into five layers:

That framing is useful because it does not reduce agent readiness to one artifact. A strong llms.txt file helps, but it is not enough by itself. A product also needs clear identity, callable surfaces, auth metadata, and usable integration paths.

Context Repo's Current Ora Score

The public Ora score page for contextrepo.com showed a score of 74/100, grade B, scanned June 7, 2026.

Ora summarized Context Repo as having strong identity and agent integration, with weaker discovery presence and public access signals. The layer breakdown shown on the score page was:

This is useful third-party evidence, but it should be read carefully.

Ora is an external scan of public signals. It does not know private implementation details, and it should not be treated as a certification of every product behavior. It is most useful as an outside-in benchmark: if an independent scanner can find a product's agent surfaces, that is evidence the surfaces are visible. If the scanner finds gaps, those gaps are useful because real agents may hit similar friction.

The Discovery score also needs product-stage context. Context Repo is brand new, so it does not yet have the third-party mentions, category visibility, search footprint, and ecosystem references that older products can accumulate over time. That limited external discoverability is different from the internal agent-discovery architecture described in this article.

The public API note is a good example. Context Repo publishes OpenAPI JSON and YAML, but the actual user-data operations are authenticated. That is the correct product behavior. The agent-discovery goal is not to make private data public. It is to make the contract, auth model, and integration paths clear before an agent tries to act.

The Context Repo Discovery Stack

Context Repo's agent-discovery surface is layered by design.

No single layer carries the whole system. The benefit comes from redundancy with purpose: multiple public surfaces describe the same product from different angles, and each surface is shaped for a different agent task.

Layer 1: Root Agent Files

The root agent files are the fastest path from "I found this domain" to "I understand what this product is."

Context Repo publishes:

• /llms.txt as the top-level AI-readable context pack.
• /llms-full.txt as the expanded context pack.
• /AGENTS.md as public operating guidance for AI agents.
• /agents.md as a lowercase-compatible route that resolves to the same agent guidance.

The root llms.txt file gives agents a compact map of the product: what Context Repo does, when to use it, when not to use it, which interfaces exist, and where to find deeper references. It points to documentation, API references, MCP setup, markdown alternates, resource articles, pricing, and discovery metadata.

AGENTS.md serves a different purpose. It is more operational. It tells agents how to interact with Context Repo, which surfaces are canonical, how authentication works, what rate limits apply, and which discovery files are available.

Together, these files reduce guesswork. An agent does not need to scrape the entire site before it can form a grounded understanding of the product.

Layer 2: Well-Known Metadata

The .well-known namespace is where agents and scanners often look for machine-readable capabilities.

Context Repo exposes several well-known discovery surfaces:

These routes help answer a different question than a homepage answers. A homepage says what the product is for. Well-known metadata says where the machine-readable contracts live, which auth model applies, and which integration surfaces are official.

Layer 3: Executable Contracts

Discovery only matters if it leads to something an agent can use.

Context Repo exposes two primary executable contract types:

The MCP server is the deepest agent-native interface. It exposes tools for prompt management, document management, collections, catalog search, OpenAI Apps SDK company-knowledge search, Deep Search, cited reasoning, and user info. Discovery methods are publicly reachable, while protected operations require OAuth or API-key authentication.

The OpenAPI contract gives agents and developers a REST-shaped way to understand the API surface. In the current codebase, the OpenAPI spec describes 30 operations. That matters because OpenAPI is a widely recognized schema for tools, SDK generators, API explorers, and agent frameworks.

MCP and OpenAPI are complementary. MCP is optimized for agent workflows and tool calling. OpenAPI is optimized for HTTP API description and developer interoperability. Publishing both gives agents more than one reliable path into the product.

Layer 4: Markdown Content Negotiation

Agents are often better at consuming markdown than full web pages.

Context Repo supports markdown-oriented access in two ways.

First, important marketing and product routes can return markdown variants when the request indicates an agent preference. If a request prefers text/markdown, or uses ?mode=agent, Context Repo redirects selected pages to agent-readable markdown versions and sets Vary: Accept.

Second, the /agent catalog itself supports both JSON and markdown. A browser or integration can request the structured JSON catalog. An agent that prefers markdown can receive a readable markdown summary of the same catalog.

This makes the same product surface useful to two audiences:

• Humans can use the normal rendered site.
• Agents can request compact, extractable markdown without reverse-engineering layout.

Resource articles also have markdown alternates, so educational content can be cited and reused by agents without requiring them to parse MDX components or rendered UI.

Layer 5: Feature Drill-Down Routes

The /agent route gives a product-level catalog. Feature-specific routes go deeper.

Context Repo exposes agent-facing drill-down routes for major product areas, including:

• Prompt management.
• Document management.
• Collections.
• Search.
• Deep Search.
• Reasoning.

These routes are useful because agents rarely need every product detail at once. An agent answering "Can Context Repo manage versioned prompts?" needs different details than an agent answering "Can Context Repo retrieve cited answers from long documents?"

Feature drill-down routes keep the top-level catalog readable while still allowing agents to fetch more detail when the user's question requires it.

Why This Is Better Than a Human-Only Website

A human-only website can still be useful to agents, but it forces them to infer structure from pages designed for people.

Context Repo's discovery model is better for agents because it separates concerns:

The result is not just better indexing. It is better recall, better source selection, and lower ambiguity when an agent needs to decide what Context Repo can actually do.

Why This Helps AEO and SEO

Answer Engine Optimization and Search Engine Optimization overlap, but they are not the same.

SEO helps web pages rank and appear in search results. AEO helps answer engines, copilots, and agents produce accurate responses about a product.

Agent discovery supports both, but its biggest value is AEO:

• It gives agents concise product summaries.
• It exposes authoritative links for citation.
• It separates marketing claims from executable capabilities.
• It tells agents where deeper documentation lives.
• It makes auth and tool boundaries explicit.
• It gives third-party scanners more public evidence to evaluate.

For SEO, these same surfaces can improve crawlability and topical clarity. For AEO, they reduce hallucination risk because the agent can retrieve official, structured facts instead of guessing from scattered pages.

What the Ora Score Proves and What It Does Not

Context Repo's Ora score is a useful external signal because it is based on public evidence. It shows that an independent agent-readiness scanner can detect strong identity, integration, and user-experience signals.

It also shows where discovery is still early. A score of 8/20 for Discovery means there is room to improve broader category visibility, developer-resource discoverability, and third-party presence. For a brand-new product, that is expected. Those signals are not solved by adding one more metadata file. They improve when public references, registries, documentation, and ecosystem mentions become easier for agents to find.

The score does not prove that every agent will use Context Repo correctly. It does not replace product evaluation, security review, or hands-on integration testing. It also does not mean every operation should be publicly executable. Context Repo intentionally keeps user data protected behind OAuth and API keys.

What the score does prove is narrower and more useful: Context Repo exposes enough public machine-readable structure for an external scanner to recognize a strong agent-readiness foundation.

The Practical Takeaway

Agent discovery is becoming part of product infrastructure.

For Context Repo, the goal is not to publish secret implementation details. The goal is to make the product legible at the right level:

• What Context Repo is.
• When agents should use it.
• Which machine-readable interfaces are official.
• How MCP, REST, markdown, and discovery metadata fit together.
• Which operations require authentication.
• Which sources agents should cite.

That is the difference between being merely present on the web and being usable by agents.

Context Repo's agent-discovery stack gives AI systems a clear path from finding the product to understanding it, citing it, and using the right interface when a user asks for help.

Read Next

• Cited Answers, Not Just Search Results
• Why Hierarchical Retrieval Finds More Than Flat RAG
• How MCP Servers Connect AI Agents to Knowledge Bases
• Context Repo on Ora.ai
• Ora.ai Methodology