The Model Context Protocol (MCP) is rapidly becoming a foundational standard for AI agents and large language models (LLMs) to access real-time tools, APIs, and data sources. But exposure alone is not enough. Unless an LLM can discover your MCP endpoint reliably, it will fall back on outdated web search or less precise retrieval methods.

In this guide we explain how LLMs discover MCP configurations, why discoverability matters for visibility and automation, and how strategic MCP deployment can make your systems first in line for AI-driven interactions.

What Discovery Means in an AI-First World

LLMs capable of acting — not just answering — rely on structured context and real services. Traditional search doesn’t give them that. MCP does. But for MCP to actually be used, the LLM first has to find it. Discovery is the bridge between static content and dynamic, agentic execution.

Without good discovery, even perfectly built MCP servers sit unused. Agents default to fallback methods such as web search, retrieval-augmented generation (RAG), or heuristic scraping, which are slower, less accurate, and often restricted by training data limitations.

The Three Rings of MCP Discovery

Modern LLM runtimes follow a prioritized discovery process that looks like concentric rings. Each “ring” represents a mechanism the orchestration layer (for example, LangChain, OpenAI Agents SDK, or Semantic Kernel) uses to locate your MCP configuration:

Ring 1 — Local Configuration

If the tool orchestrator is given explicit MCP configuration (such as server URIs and credentials in a local settings file or environment), it always uses that first. This is the most deterministic discovery mechanism and should be preferred when possible. 

Ring 2 — Authoritative Domain File

If no explicit configuration exists, LLM runtimes look for a known MCP descriptor on the brand’s own domain — typically at: https://<your-domain>/.well-known/mcp/servers.json

If this file exists and validates, it becomes the canonical discovery source for that domain. 

Ring 3 — Public or Enterprise Registry

As a fallback, orchestrators check external registries — either public catalogs (e.g., a modelcontextprotocol.io index) or corporate API inventories — for signed metadata about your MCP servers. These registries cache MCP manifests and help LLMs discover servers when Rings 1 and 2 are absent or invalid. 

What Happens After Discovery

Once an MCP manifest is located, the orchestrator converts each declared operation into a JSON Schema-based function. These definitions are injected directly into the LLM’s prompt context as callable tools. Planning logic then strongly prefers these structured operations because they are authenticated, real-time, and reliable.

This means your MCP endpoint becomes the first choice before agents ever resort to web search or RAG. Only if the MCP discovery fails or a call errors out will the model attempt fallback methods.

A Minimal MCP Discovery Example

Here’s a minimal servers.json configuration that LLM orchestrators can discover at ${domain}/.well-known/mcp/servers.json:

{
  "servers": [
    {
      "name": "Acme MCP – Production",
      "url": "https://api.acme.com/mcp",
      "version": "1.2.0",
      "auth": {
        "type": "oauth2",
        "scopes": ["profile:read","orders:read"]
      },
      "tags": ["retail", "ecommerce"]
    }
  ]
}

Hosting this metadata file on your domain allows compliant runtimes to identify and prioritize your MCP server automatically. 

Best Practices for MCP Discoverability

To maximize MCP visibility and reliability:

Host a Compliant MCP Server

Ensure your MCP implementation responds to /manifest, /health, and supported operations cleanly and consistently.

Expose the .well-known/mcp/servers.json File

Serve this file over HTTPS on every domain where your brand could be referenced. This becomes the canonical MCP descriptor for your domain. 

Register in Public or Enterprise Catalogs

Publishing your MCP metadata in a registry (e.g., registry.modelcontextprotocol.io, cloud provider catalogs) helps discovery when Rings 1 and 2 cannot be used.

Version and Scope Properly

Bump your semantic version on schema changes and keep your OAuth scopes narrow and task-specific. This ensures that orchestrators understand what your MCP server can do and when it needs to be rediscovered.

Strategic Implications

Proper MCP discovery changes how AI applications treat your system:

• Visibility becomes structural — discovery mechanics determine whether your services are even considered by an LLM.
  
  

• Execution becomes predictable — once discovered, MCP tools are invoked over typed, versioned endpoints.
  
  

• Fallbacks are secondary — AI workflows no longer default to search or heuristic retrieval unless discovery fails.
  
  

This transforms AI from a search layer into a direct execution infrastructure, reducing latency and improving reliability.