metadata

title: HFContext7
emoji: 🤗
colorFrom: pink
colorTo: yellow
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: false
tags:
  - mcp-server-track
  - Agents-MCP-Hackathon
license: apache-2.0
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

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.

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 ⚡.

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:

Create a LoRA fine-tuning script for Llama with PEFT. use hfcontext7

Set up a Gradio interface with Diffusers for image generation. use hfcontext7

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 ⚙️

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.

We took a different, more surgical approach:

Structural Pre-processing: We first clone the official documentation for major Hugging Face libraries. We parse their structure (_toctree.yml) and organize the content into a clean, hierarchical file tree. This preserves the logical layout created by the library authors.
Indexing Paths, Not Pages: Instead of embedding the full text of each page (which can be huge), we only embed the file paths. A path like Transformers/Main Classes/Trainer.md contains a wealth of semantic information about the content. This keeps our vector index small, fast, and surprisingly effective.
Two-Step Retrieval Magic: This is where the magic happens.
- 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.

Challenges along the way

Building HFContext7 wasn't straightforward. We faced a few key challenges:

The "Needle in a Haystack" Problem: The HF ecosystem is massive. A simple keyword or vector search often returns dozens of tangentially related results. Our two-step retrieval pipeline was born from the need to drastically improve precision and find that one perfect document.
Scalability & Cost: The idea of embedding the entirety of the HF docs was daunting. It would be slow to process and expensive to host. The path-embedding strategy was our answer to create a system that is both performant and cost-effective.
Taming Diverse Structures: Not all documentation is created equal. We had to write a robust parser to handle the different ways various HF projects structure their _toctree.yml files, creating a unified and navigable database. Some libraries like Hugging Face.js and Sentence Transformers use completely different documentation structures that don't follow the standard _toctree.yml format.
Content Overflow Issues: Raw markdown files often contain excessive comments, metadata, and navigation links that bloat the LLM's context window without adding value. Cleaning this content while preserving the essential information proved to be a delicate balance.
Infrastructure Limitations: We initially planned to transition to Hugging Face Inference Providers for a more integrated experience, but couldn't access the $25 HF credits during development due to credit card requirements, forcing us to stick with OpenAI's APIs for now.

Roadmap for the Future

HFContext7 is just getting started. Here's where we're headed:

🗺️ Expanded Coverage: Integrating more libraries from the Hugging Face ecosystem, including support for frameworks with non-standard documentation structures like Hugging Face.js and Sentence Transformers.
🎯 Enhanced Precision: Moving beyond single-file retrieval to identify and return the most relevant sections within a document.
🧑‍💻 Enhanced Agentic Retrieval: Building more sophisticated retrieval mechanisms that can provide broader documentation context while maintaining high accuracy, allowing for multi-document synthesis and cross-reference capabilities.
🧹 Content Optimization: Implementing smart content cleaning to remove unnecessary markdown comments, metadata, and navigation elements that waste context window space without losing critical information.
🤗 HF Native Integration: Transitioning to Hugging Face Inference Providers for embeddings and LLM calls, creating a fully integrated experience within the HF ecosystem.
🧩 Enhanced Chunking Strategy: Implementing a Context 7-inspired chunking approach that focuses on examples and creates distinct, semantically meaningful sections for each chunk, improving retrieval precision.

Available Tools

This server exposes the following tools to an MCP client:

list_huggingface_resources_names(): Returns a list of all the HF libraries and resources available for querying.
get_huggingface_documentation(topic: str, resource_names: list[str] = []): The main workhorse. Takes a topic (your question) and an optional list of resource names to search within, and returns the content of the most relevant documentation page.