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.
Enables large language models to directly access and search content in ZIM files, allowing offline question answering and information retrieval from resources like Wikipedia.
Provides Kanban, Gantt, list views, multi-project management, and archiving for task management via MCP protocol, enabling Cherry Studio Agent to create, update, query, and organize tasks.
Provides AI assistants with persistent graph-based memory capabilities using Neo4j, enabling semantic search, relationship tracking, and knowledge organization across multiple project contexts.
Universal documentation knowledge-graph MCP server with hybrid full-text + vector search. Indexes local files and remote sources from Notion, Jira, Obsidian, Linear, GitHub, and Confluence into a single SQLite knowledge graph, exposing it to AI agents via the Model Context Protocol.
A demonstration implementation of a Model Context Protocol server that provides simple mathematical tools (add, subtract) and personalized greeting resources.
Enables AI assistants to fully interact with Odoo ERP instances over XML-RPC, supporting read and write operations on any model without requiring Odoo module installation.