Enhance documentation for MCP-compatible generators by adding detailed descriptions, features, and usage scenarios. Update sections on GitHub-powered servers and improve formatting with icons for better clarity and organization.

Author: gunta

doc-generators/custom-doc-generators.mdx

@@ -65,6 +65,36 @@ Key benefits include:
   GitMCP, mentioned in the GitHub-powered documentation servers category, is actually built on top of Cloudflare AutoRAG. This demonstrates how these managed RAG solutions can be used to create production-ready MCP documentation servers with relatively little effort.
 </Info>
 
+<Info>
+  <Icon icon="lightbulb" iconType="solid" /> **How AutoRAG Works**
+  
+  AutoRAG handles two core processes:
+  
+  1. **Indexing (Asynchronous Background Process)**:
+     - **Data Ingestion**: Reads from your connected data sources
+     - **Markdown Conversion**: Converts various file types into structured Markdown
+     - **Chunking**: Splits content into smaller pieces for better retrieval
+     - **Embedding**: Transforms chunks into vectors using Workers AI models
+     - **Vector Storage**: Stores vectors and metadata in Vectorize database
+  
+  2. **Querying (Real-time Synchronous Process)**:
+     - **Query Rewriting**: Optionally improves search queries using LLMs
+     - **Query Embedding**: Transforms queries into vectors for comparison
+     - **Vector Matching**: Finds the most relevant content chunks
+     - **Response Generation**: Uses retrieved context to generate accurate answers
+</Info>
+
+<Check>
+  <Icon icon="check-double" iconType="solid" /> **Key Advantages of RAG with AutoRAG**
+  
+  - **Accuracy Without Training**: Get accurate answers based on your latest documentation without model training
+  - **Reduced Hallucinations**: Ground AI responses in your actual documentation
+  - **Source Attribution**: Trace responses back to specific documentation sources
+  - **Knowledge Updates**: Documentation updates are automatically reflected in responses
+  - **Efficient Context Usage**: Only relevant information is retrieved, maximizing context window usage
+  - **Image Processing**: Can extract text and context from images in your documentation
+</Check>
+
 <CardGroup cols={2}>
   <Card title="Automated Indexing" icon="robot" iconType="solid">
     Set up your data sources once and AutoRAG will continuously monitor and index new content, keeping your documentation current.
@@ -78,16 +108,38 @@ Key benefits include:
 
 To create your own MCP documentation server using AutoRAG:
 
-1. **Configure Data Sources**: Connect your documentation repositories, websites, or content stores
-2. **Set Up Indexing**: Configure chunking, embedding, and indexing settings
-3. **Define Retrieval Parameters**: Customize relevance thresholds and context window parameters
-4. **Implement MCP Protocol**: Wrap the AutoRAG responses in the MCP protocol format
-5. **Deploy and Monitor**: Launch your server and monitor performance
+<Steps>
+  <Step title="Configure Data Sources">
+    Connect your documentation repositories, websites, or content stores. AutoRAG supports various sources including websites, GitHub repositories, and file storage systems.
+  </Step>
+  
+  <Step title="Set Up Indexing">
+    Configure chunking, embedding, and indexing settings. Determine how your content will be processed, including chunk size and overlap for optimal retrieval.
+  </Step>
+  
+  <Step title="Define Retrieval Parameters">
+    Customize relevance thresholds and context window parameters. Set the number of chunks to retrieve and minimum similarity scores to ensure accurate results.
+  </Step>
+  
+  <Step title="Implement MCP Protocol">
+    Wrap the AutoRAG responses in the MCP protocol format. Create endpoints that follow the MCP specification to enable seamless integration with AI assistants.
+  </Step>
+  
+  <Step title="Deploy and Monitor">
+    Launch your server and monitor performance. Track key metrics like query latency, relevance scores, and user satisfaction to continuously improve your documentation system.
+  </Step>
+</Steps>
 
 <Tip>
   Start with a small subset of your documentation to test the system before scaling to your entire knowledge base.
 </Tip>
 
+<Info>
+  <Icon icon="book" iconType="solid" /> **Further Resources**
+  
+  For complete implementation details, configuration options, and API references, be sure to follow the official [Cloudflare AutoRAG documentation](https://developers.cloudflare.com/autorag/). The documentation provides in-depth guides on data sources, indexing settings, query rewriting, similarity caching, and other advanced features.
+</Info>
+
 ## <Icon icon="circle-check" iconType="solid" color="#10B981" /> When to Choose Custom Solutions
 
 Custom documentation generators are most valuable when:

doc-generators/github-powered-servers.mdx

@@ -5,17 +5,21 @@ icon: "github"
 iconType: "brands"
 ---
 
-Sometimes you don't *write* the docs yourself – you simply want the full, up‑to‑date documentation of an **open‑source library on GitHub** available to your AI assistant. The community projects below turn *any* repository into a remote MCP server that streams documentation on demand.
+Sometimes you don't *write* the docs yourself – you simply want the full, up‑to‑date documentation of an **open‑source library on GitHub** available to your AI assistant. The community projects below turn **_any_ repository into a remote MCP server** that streams documentation on demand.
 
 <Callout type="warning">
   <Icon icon="clock" iconType="solid" /> **First-Time Processing** – If a repository hasn't been previously indexed by Context7 or GitMCP, it may take some time (typically a few minutes) for the initial processing to complete as the system performs autoindexing and RAG preparation of the content.
 </Callout>
 
-## Available Tools
+## <Icon icon="toolbox" iconType="solid" /> Available Tools
 
 ### <Icon icon="rocket" iconType="solid" /> Context7
 
-Context7 automatically ingests popular open‑source libraries (and many private ones too) and exposes them as remote MCP servers.
+**Context7** automatically ingests **popular open‑source libraries** (and many **private ones** too) and exposes them as **remote MCP servers**.
+
+<Callout type="info">
+  <Icon icon="bolt" iconType="solid" /> **Quick Setup** – Use the configuration below to get started with Context7 in your preferred environment.
+</Callout>
 
 <Tabs items={['Cursor', 'Windsurf', 'VSCode', 'Claude Desktop']}>
   <Tab title="Cursor">
@@ -79,7 +83,7 @@ Context7 automatically ingests popular open‑source libraries (and many private
   </Tab>
 </Tabs>
 
-#### Example: Using Context7 with Three.js
+#### <Icon icon="lightbulb" iconType="solid" /> Example: Using Context7 with Three.js
 
 After installing Context7, you can prompt your AI assistant to use Three.js documentation like this:
 
@@ -88,11 +92,15 @@ Can you help me create a basic Three.js scene with a rotating cube?
 Use context7
 ```
 
-The AI will then dynamically query Context7 for relevant Three.js documentation as it helps you.
+The AI will then **dynamically query Context7** for relevant Three.js documentation as it helps you.
 
 ### <Icon icon="code-branch" iconType="solid" /> GitMCP.io
 
-[GitMCP](https://gitmcp.io) lets you turn **any public GitHub repository** into a fully‑featured MCP server just by changing the domain from `github.com` → `gitmcp.io` (or `github.io` → `gitmcp.io`).  No build step, no package installs.
+[**GitMCP**](https://gitmcp.io) lets you turn **any public GitHub repository** into a fully‑featured MCP server just by changing the domain from `github.com` → `gitmcp.io` (or `github.io` → `gitmcp.io`).  **No build step, no package installs.**
+
+<Callout type="tip">
+  <Icon icon="wand-magic-sparkles" iconType="solid" /> **Pro Tip** – Simply change GitHub URLs to GitMCP URLs to instantly access documentation!
+</Callout>
 
 <Tabs items={['Cursor', 'Windsurf', 'VSCode', 'Claude Desktop']}>
   <Tab title="Cursor">
@@ -146,32 +154,34 @@ The AI will then dynamically query Context7 for relevant Three.js documentation
   </Tab>
 </Tabs>
 
-## Example: Three.js Documentation
+## <Icon icon="vial" iconType="solid" /> Example: Three.js Documentation
 
 Once either MCP server is configured, the following **single‑shot prompt** in Cursor + Claude illustrates the improvement:
 
 > Build a Three.js scene featuring a controllable realistic person navigating a textured dynamic urban environment with realistic lighting and subtle bloom effects. Ensure keyboard controls (WASD) for movement.
 
-### Side‑by‑Side Result (GitMCP vs. No GitMCP)
+### <Icon icon="code-compare" iconType="solid" /> Side‑by‑Side Result (GitMCP vs. No GitMCP)
 
 <iframe
   src="gitmcp-threejs.mp4"
   title="GitMCP vs No GitMCP – Three.js demo"
   width="550"
   height="206"
+  allow="autoplay; fullscreen;"
+  autoplay
   style={{ width: '100%', height: 'auto', aspectRatio: '2872/1080', border: '0', borderRadius: '0.5rem' }}
 ></iframe>
 
 <Callout type="info">
   <Icon icon="info-circle" iconType="solid" /> **Why it matters** – by streaming the full Three.js documentation on demand, GitMCP gives the LLM precise API details (e.g. `GLTFLoader`, `DirectionalLight.castShadow`, `EffectComposer`) that would otherwise be unavailable or outdated, resulting in dramatically higher‑quality code generation.
 </Callout>
 
-## Benefits of GitHub-Powered MCP Servers
+## <Icon icon="star" iconType="solid" /> Benefits of GitHub-Powered MCP Servers
 
 These tools offer several advantages:
 
-- **Zero Documentation Effort**: Use existing documentation from any GitHub repository
-- **Always Up-to-Date**: Documentation is pulled directly from the latest repository version
-- **On-Demand Access**: AI assistants query only what they need when they need it
-- **Broad Compatibility**: Works with virtually any open-source project on GitHub
-- **Simplified Integration**: Easy to add to your IDE or AI assistant 
+- <Icon icon="check" iconType="solid" /> **Zero Documentation Effort**: Use existing documentation from any GitHub repository
+- <Icon icon="newspaper" iconType="solid" /> **Always Up-to-Date**: Documentation is pulled directly from the latest repository version
+- <Icon icon="bolt" iconType="solid" /> **On-Demand Access**: AI assistants query only what they need when they need it
+- <Icon icon="puzzle-piece" iconType="solid" /> **Broad Compatibility**: Works with virtually any open-source project on GitHub
+- <Icon icon="code" iconType="solid" /> **Simplified Integration**: Easy to add to your IDE or AI assistant 

doc-generators/mcp-compatible-generators.mdx

@@ -5,33 +5,55 @@ icon: "star"
 iconType: "solid"
 ---
 
-MCP-compatible documentation generators are specialized tools designed with features that support documenting AI-related projects, specifically those following the Machine-Controlled Program (MCP) pattern.
-
-## Available Generators
-
-- <Icon icon="leaf" iconType="solid" /> **[Mintlify](https://mintlify.com/)**: A modern documentation platform used by this site, with built-in support for AI-related content and interactive examples
-
-- <Icon icon="microphone-alt" iconType="solid" /> **[Speakeasy](https://www.speakeasyapi.dev/)**: API documentation generator with special features for documenting AI endpoints and capabilities
-
-- <Icon icon="shield-alt" iconType="solid" /> **[Stainless](https://stainlessapi.com/)**: Documentation tooling focused on robust API references and SDK documentation for MCP services
-
-## Features and Benefits
-
-These documentation tools provide specialized features for describing, demonstrating, and documenting the unique aspects of MCP applications like:
-
-- API endpoints for AI services
-- Model capabilities and limitations
-- AI interaction patterns and prompting strategies
-- Integration guidelines for LLM-powered applications
-- Context window management documentation
-- Tool specifications and schemas
-
-## When to Use
-
-Choose these specialized documentation generators when:
-
-- Building AI-first applications that need clear documentation
-- Creating API reference materials for LLM services
-- Documenting prompt engineering guidelines
-- Providing examples of AI tool usage patterns
-- Sharing best practices for MCP integration 
+MCP-compatible documentation generators are specialized tools designed with features that support documenting AI-related projects, specifically those following the **Machine-Controlled Program (MCP)** pattern.
+
+## <Icon icon="tools" iconType="solid" /> Available Generators
+
+- <Icon icon="leaf" iconType="solid" color="#22c55e" /> **[Mintlify](https://mintlify.com/)**: A modern documentation platform used by this site, with built-in support for AI-related content and interactive examples. Mintlify can generate MCP servers directly from your documentation, enabling:
+  - <Icon icon="robot" iconType="solid" /> **AI-accessible documentation** for contextual answers
+  - <Icon icon="bolt" iconType="solid" /> **Real-time API querying** through OpenAPI specifications
+  - <Icon icon="code" iconType="solid" /> **Structured formats** like `/llms.txt` for AI-friendly indexing
+  - <Icon icon="file-lines" iconType="solid" /> **Precompiled context files** for LLMs to efficiently access your documentation
+  
+  <Note>
+    <Icon icon="circle-info" iconType="solid" /> This entire website is generated using **Mintlify** and can be accessed through its autogenerated MCP docs server - providing a meta example of MCP documentation that documents MCP tools.
+  </Note>
+
+- <Icon icon="microphone" iconType="solid" color="#3b82f6" /> **[Speakeasy](https://www.speakeasyapi.dev/)**: API documentation generator with special features for documenting AI endpoints and capabilities
+
+- <Icon icon="shield" iconType="solid" color="#ef4444" /> **[Stainless](https://stainlessapi.com/)**: Documentation tooling focused on robust API references and SDK documentation for MCP services
+
+## <Icon icon="list-check" iconType="solid" /> Features and Benefits
+
+These documentation tools provide **specialized features** for describing, demonstrating, and documenting the unique aspects of MCP applications like:
+
+<CardGroup >
+  <Card icon="wifi" title="API Endpoints">
+    Comprehensive documentation for AI services
+  </Card>
+  <Card icon="brain" title="Model Capabilities">
+    Document capabilities and limitations clearly
+  </Card>
+  <Card icon="message-bot" title="Interaction Patterns">
+    AI interaction patterns and prompting strategies
+  </Card>
+  <Card icon="puzzle-piece" title="Integration Guidelines">
+    Best practices for LLM-powered applications
+  </Card>
+  <Card icon="expand" title="Context Management">
+    Window management documentation for LLMs
+  </Card>
+  <Card icon="screwdriver-wrench" title="Tool Specifications">
+    Schemas and usage details for AI tools
+  </Card>
+</CardGroup>
+
+## <Icon icon="clock" iconType="solid" /> When to Use
+
+Choose these **specialized documentation generators** when:
+
+- <Icon icon="rocket" iconType="solid" /> Building **AI-first applications** that need clear documentation
+- <Icon icon="book-open-reader" iconType="solid" /> Creating **API reference materials** for LLM services
+- <Icon icon="message" iconType="solid" /> Documenting **prompt engineering guidelines**
+- <Icon icon="code-compare" iconType="solid" /> Providing **examples of AI tool usage patterns**
+- <Icon icon="share-nodes" iconType="solid" /> Sharing **best practices for MCP integration**