Analyze and trace code execution paths with semantic understanding, identifying data flows and dependencies to debug complex distributed systems efficiently.
Create a vector index for semantic code search by generating embeddings with tree-sitter and Jina AI, enabling efficient and accurate querying of source code files.
Find code by meaning using semantic understanding combined with exact text matching. Search with natural language queries like 'authentication logic' or 'database queries' to locate relevant snippets with file locations and line numbers.
Search and retrieve stored memories using semantic understanding, filter by source type or bucket, and sort results by date or relevance for precise information retrieval.
Locate specific code symbols like functions, classes, or variables in a project directory using semantic analysis. Enables quick navigation and understanding of code structure in Hi-AI development workflows.
Analyzes files or directories to list top-level code symbols (name_path, kind), providing a high-level understanding for targeted reading, searching, or editing operations. Returns a JSON object with symbol details.
CodeGuard MCP is a real-time AI code security scanning tool used to detect vulnerabilities, keys, and compliance issues in AI-generated code, and is suitable for code security reviews in development environments
Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.
A custom MCP tool that integrates Perplexity AI's API with Claude Desktop, allowing Claude to perform web-based research and provide answers with citations.