FS Query

About

Traditional AI assistants often consume entire codebases as plain text, leading to massive token consumption and irrelevant context. FS Query revolutionizes this by acting as a Model Context Protocol (MCP) server, leveraging Tree-sitter to surgically extract specific code symbols such as functions, classes, and variables. This allows AI assistants to understand code structure, query for precise information, and dramatically reduce token usage, making interactions more efficient and relevant.

Key Features

• 1 GitHub stars
• Model Context Protocol (MCP) server integration for AI assistants
• Precise code symbol extraction using Tree-sitter parsing
• Supports multiple programming languages including Rust, Python, C++, JavaScript/TypeScript, and Go
• Filters extracted symbols by type (functions, classes, structs, variables, enums, traits/interfaces)
• Command-line interface (CLI) for standalone code analysis and symbol extraction

Use Cases

• Enabling AI assistants to query specific code elements (e.g., 'Show me all classes') without full file ingestion.
• Providing AI with precise context about functions, variables, or structs to answer coding questions efficiently.
• Reducing token consumption and improving relevance for AI assistants interacting with codebases.