Paper2Code - chunhualiao/public-docs GitHub Wiki

"DeepCode: Open Agentic Coding (Paper2Code & Text2Web & Text2Backend)"

The DeepCode repository uses the following MCP (Model Context Protocol) servers and tools:

brave: Web Search Engine for real-time information retrieval via Brave Search API.
bocha-mcp: Alternative Search with independent API access.
filesystem: For local file and directory management, read/write operations.
fetch: For web content retrieval from URLs and web resources.
github-downloader: For cloning and downloading GitHub repositories for analysis.
file-downloader: For downloading and converting files (PDF, DOCX, etc.) to Markdown.
command-executor: For executing bash/shell commands for environment management.
code-implementation: Comprehensive code reproduction with execution and testing.
code-reference-indexer: Intelligent indexing and search of code repositories.
document-segmentation: Intelligent document segmentation for large papers and technical documents.

Here’s a detailed breakdown of the three MCP (Model Context Protocol) servers used by the DeepCode repository, as requested:

Purpose: This MCP server is designed to reproduce, execute, and test code comprehensively. It enables DeepCode to take code snippets, scripts, or entire programs, run them in a controlled environment, and validate their functionality through automated testing.
Functionality:
- Code Reproduction: It can take code from a repository or input and recreate it in a sandboxed environment to ensure it behaves as expected.
- Execution: Supports running code in various programming languages (e.g., Python, JavaScript, Java) by setting up necessary dependencies and environments.
- Testing: Integrates testing frameworks (e.g., pytest for Python, JUnit for Java) to execute unit tests, integration tests, or custom test cases. It verifies code correctness, performance, and edge cases.
- Error Handling: Identifies syntax errors, runtime issues, or logical bugs and provides detailed feedback for debugging.
- Use Case: For example, if DeepCode is analyzing a machine learning model’s code, this server can execute the code, run it with sample data, and validate outputs against expected results.
Relevance to DeepCode: This is critical for validating the functionality of code in the repository, ensuring that algorithms or models (e.g., for code understanding or generation) work as intended.

Purpose: This MCP server provides intelligent indexing and search capabilities for codebases, enabling efficient navigation and retrieval of code-related information across repositories.
Functionality:
- Indexing: Creates a searchable index of code files, including functions, classes, variables, and comments. It uses techniques like AST (Abstract Syntax Tree) parsing to understand code structure.
- Search: Supports semantic and keyword-based search, allowing users to find specific code snippets, functions, or modules based on functionality or description (e.g., “find all functions that implement graph traversal”).
- Context Awareness: Leverages natural language processing (NLP) to understand code comments, documentation, and naming conventions, making searches more intuitive.
- Cross-Repository Analysis: Can index multiple repositories (e.g., those cloned via the github-downloader MCP) to provide a unified search experience.
- Use Case: For a researcher using DeepCode, this server could help locate specific implementations of a neural network layer across multiple open-source repositories.
Relevance to DeepCode: This enhances DeepCode’s ability to analyze and understand large codebases, making it easier to identify patterns, dependencies, or reusable components in code for tasks like code summarization or recommendation.

Purpose: This MCP server focuses on breaking down large documents (e.g., research papers, technical reports, or documentation) into meaningful segments for easier analysis and processing.
Functionality:
- Segmentation: Uses NLP and machine learning to divide documents into logical sections (e.g., abstract, introduction, methodology, results) based on content, headings, or semantic cues.
- Content Analysis: Extracts key information like equations, tables, or code snippets embedded in documents, making them accessible for further processing.
- Metadata Extraction: Identifies metadata such as authors, publication dates, or references to improve document organization and retrieval.
- Context Preservation: Ensures segments retain contextual relationships, so segmented parts can be analyzed individually or as part of the whole document.
- Use Case: For a PDF research paper on deep learning, this server could split the document into sections, extract code snippets for analysis by code-implementation, and summarize key findings.
Relevance to DeepCode: This is particularly useful for processing technical documentation or papers in the DeepCode repository, enabling the system to extract and analyze code-related content from academic or technical sources.

These MCP servers complement each other to support DeepCode’s goals of understanding, analyzing, and generating code. For instance:

A researcher might use DeepCode to analyze a GitHub repository. The code-reference-indexer indexes the repository to find relevant code snippets. The code-implementation server then executes and tests those snippets to verify functionality. If the repository includes documentation (e.g., a README or a linked research paper), the document-segmentation server processes it to extract relevant sections or code examples for further analysis.

If you’d like more specifics (e.g., technical implementation details, supported languages, or examples from the DeepCode repository), let me know!