A TypeScript tool that ranks files in your codebase by importance, tracks dependencies, and provides file summaries to help understand code structure through Cursor's Model Context Protocol.
Code graph context engine that parses codebases with tree-sitter (170+ languages), builds structural dependency graphs, and provides 24 MCP tools for code intelligence. One prepare_context call gives your AI agent the right files for any task. Includes focus, blast radius, hotspots, dead code detection, and hybrid search.
Extract domain knowledge from codebases to reduce LLM token consumption by 20x and time in agentic search by 10x — gathers and makes concepts, naming conventions, and vocabulary queryable via MCP.
Multi-language code-graph MCP server with 18 tools for structural code queries — find_symbol,
callers, callees, blast_radius, dead_code, and cross-stack dataflow_trace from HTTP request through
service layers to SQL. Tree-sitter parsing for Python, TypeScript, JavaScript, and Go;
local-first, no API key required.
A knowledge graph MCP server for Moleculer and Go microservice projects that scans codebases, extracts service metadata, seeds Neo4j, and exposes tools for LLM clients to query and understand service architecture.
Local-first cross-service code intelligence engine for AI agents, connecting frontend, gateways, backend services, and databases to enable impact analysis and change planning.
A high-performance code knowledge graph server implementing MCP, indexing codebases into a structured AST knowledge graph with semantic search, call graph traversal, and HTTP route tracing.
Indexes a mono-repo into a knowledge graph and provides MCP tools to query code structure—packages, components, routes, HTTP calls—without file reads or grep round-trips.