code context
Basic Information
This repository provides an MCP plugin that enables semantic code search for AI coding agents. It is built to integrate with agents such as Claude Code, Gemini CLI, Cursor, and other similar coding assistants to supply relevant code context during coding workflows. The plugin is intended to surface semantically similar code snippets and repository context so agents can answer code queries, perform context-aware completions, and reason about code with grounded examples. The README content available in the repository is minimal, so implementers should consult the repository files for integration and deployment details. The project targets developers and teams who want to augment LLM-driven coding tools with retrieval-based code context.
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App Details
Features
Semantic code search delivered as an MCP plugin that can be integrated into multiple AI coding agents. Explicit compatibility signals mention Claude Code, Gemini CLI, and Cursor, indicating multi-agent support. The plugin focuses on returning relevant code snippets and nearby file context to enrich agent prompts. It is designed to plug into existing agent pipelines rather than replace agent logic. Documentation in the provided README is limited, so exact API shapes, indexing mechanisms, and configuration options should be verified in the repository itself. The design emphasizes enabling context-aware retrieval for coding tasks.
Use Cases
By adding semantic code search to coding agents, this plugin helps developers and teams find relevant code examples, API usages, and implementation patterns without manual searching. It improves the grounding of agent responses, leading to more accurate code generation, faster debugging, and more efficient code review and comprehension. Integrations with multiple agent platforms make it reusable across tooling ecosystems. Because the README is sparse, users should inspect the repo for setup and operational details, but the primary benefit is reducing time spent locating actionable code context and improving the quality of AI-assisted development.
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