minor edits

README.md

@@ -16,18 +16,20 @@ short_description: Latest 🤗 documentation for LLMs and AI code editors
 
 # HFContext7: Up-to-date 🤗 Docs for your LLM
 
-### The Problem: Your LLM is stuck in the past
+## The Problem: Your LLM is stuck in the past
 
 You ask your AI assistant for a code snippet using the latest `diffusers` feature, and it confidently spits out code that was deprecated six months ago. You're trying to debug a `transformers` pipeline, and the LLM hallucinates parameters that don't exist. Sound familiar?
 
-Large Language Models are powerful, but their knowledge is frozen in time. The Hugging Face ecosystem, however, moves at lightning speed. This knowledge gap leads to wasted time, frustrating debugging sessions, and a reliance on constant tab-switching to find the right documentation page.
+Large Language Models are powerful, but their knowledge is frozen in time ⏰. The Hugging Face ecosystem, however, moves at lightning speed. This knowledge gap leads to wasted time, frustrating debugging sessions 😤, and a reliance on constant tab-switching 🔄 to find the right documentation page.
 
-### The Solution: Fresh Docs, Right in Your Prompt
+## The Solution: Fresh Docs, Right in Your Prompt
 
-**HFContext7** is a Model Context Protocol (MCP) server that acts as a bridge between your AI assistant and the ever-evolving Hugging Face documentation. It provides your LLM with the ability to fetch the **single most relevant** documentation page for your query, ensuring the context it uses is fresh, accurate, and directly from the source.
+**HFContext7** is a Model Context Protocol (MCP) server that acts as a bridge between your AI assistant and the ever-evolving Hugging Face documentation. It provides your LLM with the ability to fetch the **single most relevant** documentation page for your query, ensuring the context it uses is fresh, accurate, and directly from the source ⚡.
 
 **Inspired by the (unfortunately closed-source) `Context7` project**, we wanted to build an open-source alternative focused specifically on the rich, complex, and rapidly changing Hugging Face ecosystem.
 
+Demo Link: https://youtu.be/O3QFfPo9DcM
+
 Simply add `use hfcontext7` to your prompt:
 
 ```txt
@@ -38,11 +40,13 @@ Create a LoRA fine-tuning script for Llama with PEFT. use hfcontext7
 Set up a Gradio interface with Diffusers for image generation. use hfcontext7
 ```
 
-HfContext7 instantly provides your AI assistant with accurate, up-to-date HuggingFace documentation and code examples.
+Was the documentation helpful in answering my query?
+
+HFContext7 instantly provides your AI assistant with accurate, up-to-date HuggingFace documentation and code examples 🎯.
 
 ---
 
-### Under the Hood: A Smarter RAG Pipeline
+## Under the Hood: A Smarter RAG Pipeline ⚙️
 
 Traditional RAG (Retrieval-Augmented Generation) on large documentation sets can be slow, expensive, and imprecise. Embedding entire libraries' worth of content leads to massive vector databases and often returns noisy, irrelevant chunks.
 
@@ -56,11 +60,11 @@ We took a different, more surgical approach:
     *   **Step 1: Candidate Search:** When you ask a question, we embed your query and perform a semantic search against our index of *file paths*. This instantly gives us the top 50 most likely documentation pages.
     *   **Step 2: LLM-Powered Selection:** We don't just dump all 50 files into the context. Instead, we generate a `tree`-like view of their file structure and present it to a powerful LLM (GPT-4o) along with your original question. The LLM's only job is to analyze this structure and choose the **one file** that is the most likely to contain the answer.
 
-This approach is fast, cheap, and highly precise. It leverages the inherent structure of good documentation and uses a powerful reasoning engine for the final selection, ensuring you get the whole, relevant page, not just a random chunk.
+This approach is fast, cheap, and highly precise 🚀. It leverages the inherent structure of good documentation and uses a powerful reasoning engine for the final selection, ensuring you get the whole, relevant page, not just a random chunk.
 
 ---
 
-### Challenges along the way
+## Challenges along the way
 
 Building HFContext7 wasn't straightforward. We faced a few key challenges:
 
@@ -72,7 +76,7 @@ Building HFContext7 wasn't straightforward. We faced a few key challenges:
 
 ---
 
-### Roadmap for the Future
+## Roadmap for the Future
 
 HFContext7 is just getting started. Here's where we're headed:
 
@@ -85,7 +89,7 @@ HFContext7 is just getting started. Here's where we're headed:
 
 ---
 
-### Available Tools
+## Available Tools
 
 This server exposes the following tools to an MCP client:
 

app.py

@@ -166,8 +166,6 @@ list_resources_demo = gr.Interface(
     fn=list_huggingface_resources_names,
     inputs=[],
     outputs="json",
-    title="HuggingFace Ecosystem Explorer",
-    description="Explore the names of the libraries, services, and other resources available within the HuggingFace ecosystem",
 )
 
 get_docs_demo = gr.Interface(