$ loading_
Build code graphs and semantic indexes for fast code querying and impact analysis.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "UltraCode" yet — see the docs or source repo.
Using the current repository graph, find the main functions, classes, and modules that paymentService.processOrder depends on, and list them by call chain.
A list of related code entities and their dependency relationships for understanding the call chain.
If I change the definition of the email field in the User model, analyze the functions, interfaces, and upstream/downstream modules that may be affected.
An impact map of affected code areas and relationships to help evaluate change risk.
Find all entities related to authorization checks in the codebase, including functions, classes, and modules, and rank them by relevance.
A relevance-ranked list of code entities to quickly locate related implementations.
When taking over a large project, developers can use the code structure graph to quickly understand key entities and their relationships, reducing time spent reading source code. For complex modules, dependency-chain queries help locate core logic faster.
Before refactoring functions, classes, or data models, teams can run impact analysis to see which code entities and relationships may be affected. This helps identify risks early and plan test scope.
When AI coding agents need to understand an existing repository, this MCP tool provides structured querying and semantic indexing. Agents can use it to find relevant code entities faster and analyze context.
It is an MCP server for AI coding agents that builds a complete code structure graph and a semantic vector index. This enables fast querying of code entities, relationships, and impact analysis.
It is suitable for querying relationships among code entities such as functions, classes, and modules, as well as change impact analysis and semantic code search. It is especially useful for quickly understanding large codebases.
The provided information does not include installation steps, runtime requirements, or key requirements. Please see the source repository for details.
Build MCP servers once and connect them to multiple AI agents.
Analyze codebases with semantic search, dependency insights, and natural language Q&A.
Search repositories semantically and turn codebases into AI-ready context and knowledge.
Give AI coding agents persistent code memory while cutting token usage dramatically.
Retrieve GitHub code at symbol level to explore repos with far lower AI token costs.
Give AI coding assistants memory, code graph insight, and safe multi-agent coordination.