Docs_mcp: Smart MCP Tool for Bevy Docs Similarity Search in Dev

Docs MCP

An MCP tool for similarity search based on Bevy's English documentation. It realizes intelligent document query functions by converting HTML documents to Markdown and storing them in a vectorized form.

Search tools Developer tools #Document Search #Vector Database #Game Engine #Intelligent Assistant .Python

rating : 2.5 points

downloads : 0

update time : 2025-12-29

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What is the Bevy Docs MCP Search Tool?

This is an intelligent search tool specifically designed for Bevy game engine documentation. It uses advanced vector search technology to understand the semantic meaning of your query, rather than just keyword matching. When you encounter problems while developing games with Bevy, you can quickly find the most relevant official documentation content through this tool.

How to Use the Bevy Docs MCP Search Tool?

You can use this tool through an AI assistant that supports the MCP protocol (such as Cherry Studio). Simply describe the Bevy-related problem you encountered or the concept you need to understand to the assistant, and the assistant will automatically search the Bevy documentation and return the most relevant information. You don't need to manually browse a large number of documentation pages during the whole process.

Applicable Scenarios

This tool is particularly useful when you are learning the Bevy framework, encounter API usage problems during game development, need to understand the working principle of specific components, or want to find best practice examples. It is especially suitable for developers and beginners who don't want to manually search through a large amount of documentation.

Main Features

Semantic Similarity Search

Uses vector database technology to understand the deep meaning of the query, rather than just keyword matching. Even if you use different terms to describe the problem, you can still find relevant documentation.

Offline Documentation Support

Based on locally converted Markdown format documentation, it can be searched without an internet connection, ensuring search speed and data privacy.

MCP Protocol Integration

Follows the Model Context Protocol standard and can be seamlessly integrated with various AI assistants and development tools that support MCP.

English Documentation Optimization

Specifically optimized for Bevy's official English documentation to ensure the accuracy and relevance of search results.

Advantages

Intelligent search: More accurate than traditional keyword search and can understand the intention of the problem

Development efficiency: Significantly reduces the time spent on finding documentation and allows you to focus more on coding

Easy integration: Integrated with the existing development toolchain through the standard MCP protocol

Available offline: Does not rely on the network and protects code and query privacy

Limitations

Requires initial setup: You need to convert the documentation and build a vector database first

Only for English documentation: Currently mainly targeted at Bevy's official English documentation

Depends on MCP support: Needs to be used in an AI assistant environment that supports MCP

Documentation update delay: You need to manually update the documentation database to get the latest content

How to Use

Prepare Documentation Data

Convert Bevy's official HTML documentation to Markdown format, and then run a script to load it into the Milvus vector database.

Start the MCP Server

Run the MCP server program and ensure that the Milvus database connection information is correctly configured.

Configure the AI Assistant

Add this MCP server to Cherry Studio or other AI assistants that support MCP, and set appropriate prompt words.

Start Searching

Enter your Bevy-related questions in the AI assistant, and the assistant will automatically search and return relevant documentation content.

Usage Examples

Learning the Bevy ECS System

When you are just starting to learn the Entity Component System (ECS) architecture of Bevy, you can ask questions to understand the basic concepts and usage methods.

Solving Rendering Problems

When you encounter graphics rendering problems during development, you can quickly find relevant APIs and solutions.

Finding API Usage

When you know the name of an API but are not sure about its specific usage, you can quickly get detailed usage instructions and parameter information.