Discovery Mechanisms

Organization

RedHatProductSecurity

discovery-mechanisms

Enforce support for OAuth 2.0 and OIDC discovery mechanisms in MCP clients. Use when building, configuring, or reviewing MCP client authentication and authorization server discovery.

Overview

Publisher	RedHatProductSecurity
Repository	`prodsec-skills`
Skill name	`discovery-mechanisms`
Stars	56
Forks	9
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by RedHatProductSecurity on GitHub. Read the source before you install it.

Installation

Install the Discovery Mechanisms AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/module/skills/discovery-mechanisms
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/module/skills/discovery-mechanisms

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/RedHatProductSecurity/prodsec-skills.git /tmp/prodsec-skills
mkdir -p .claude/skills
cp -r /tmp/prodsec-skills/module/skills/discovery-mechanisms .claude/skills/discovery-mechanisms

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Discovery Mechanisms in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Discovery Mechanisms on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Discovery Mechanisms is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Discovery Mechanisms for MCP Clients

Security Requirement

MCP clients MUST support both of the following discovery mechanisms for locating authorization servers:

OAuth 2.0 Authorization Server Metadata (RFC 8414)
OpenID Connect Discovery 1.0

Supporting both mechanisms ensures interoperability with any authorization server an MCP server may designate.

OAuth 2.0 Authorization Server Metadata (RFC 8414)

The client fetches metadata from /.well-known/oauth-authorization-server:

json
{
  "issuer": "https://auth.example.com",
  "authorization_endpoint": "https://auth.example.com/authorize",
  "token_endpoint": "https://auth.example.com/token",
  "scopes_supported": ["tools:read", "tools:execute"],
  "response_types_supported": ["code"],
  "grant_types_supported": ["authorization_code", "client_credentials"],
  "code_challenge_methods_supported": ["S256"]
}

OpenID Connect Discovery 1.0

The client fetches metadata from /.well-known/openid-configuration:

json
{
  "issuer": "https://auth.example.com",
  "authorization_endpoint": "https://auth.example.com/authorize",
  "token_endpoint": "https://auth.example.com/token",
  "userinfo_endpoint": "https://auth.example.com/userinfo",
  "jwks_uri": "https://auth.example.com/.well-known/jwks.json",
  "scopes_supported": ["openid", "profile", "tools:read"],
  "response_types_supported": ["code"],
  "id_token_signing_alg_values_supported": ["RS256"]
}

Discovery Flow


1. MCP client connects to MCP server
2. MCP client fetches Protected Resource Metadata from MCP server
3. Extract authorization_servers from the metadata
4. For each authorization server, attempt discovery:
   a. Try /.well-known/openid-configuration
   b. Try /.well-known/oauth-authorization-server
   c. Use whichever succeeds (prefer OIDC if both available)
5. Use discovered endpoints for authentication flows

Implementation Checklist

Implement fetching and parsing of /.well-known/openid-configuration
Implement fetching and parsing of /.well-known/oauth-authorization-server
Handle both metadata formats and extract required endpoints
Cache discovery metadata with appropriate TTL
Validate issuer in metadata matches the expected authorization server URL
Handle network errors and fallback between discovery mechanisms

Frequently asked questions

What does the Discovery Mechanisms AI skill do?

Enforce support for OAuth 2.0 and OIDC discovery mechanisms in MCP clients. Use when building, configuring, or reviewing MCP client authentication and authorization server discovery.

Why use Discovery Mechanisms on TypingMind?

Because you install it once and use it with any model. Discovery Mechanisms is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Discovery Mechanisms in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/module/skills/discovery-mechanisms. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Discovery Mechanisms?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Discovery Mechanisms?

As many as you like. As long as a model supports skills, you can use Discovery Mechanisms with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Discovery Mechanisms AI skill free?

Yes. It is published on GitHub by RedHatProductSecurity under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Discovery Mechanisms for MCP Clients

Security Requirement

OAuth 2.0 Authorization Server Metadata (RFC 8414)

OpenID Connect Discovery 1.0

Discovery Flow

Implementation Checklist

Frequently asked questions

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