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Local hybrid search and SQL aggregation for code agents.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.infino-ai/code-context" MCP server from askskill: Run: claude mcp add 'io-github-infino-ai-code-context' -- npx -y @infino-ai/code-context
Search the current repo for authentication/login-related implementations, prioritizing the most relevant files, functions, and call chains.
Return relevant code locations with context summaries.
Using the repo index, run a SQL aggregation for the "payment flow" topic and summarize file counts, main modules, and frequent keywords.
Output aggregated statistics for the topic.
Find where a core function is called in the repo, and organize the most common upstream and downstream relationships.
Provide call relationships and a list of related snippets.
When a developer joins an unfamiliar project, it helps quickly search the local repository for relevant implementations and reduces manual browsing. It also turns scattered code clues into readable context.
When you need to understand which files, modules, or keywords cover a topic, SQL aggregation over the repo index can provide quick statistics. It is useful for code inventory and scope checks.
Before debugging or making changes, users can use hybrid search to find the most relevant code snippets and context, then decide where to edit next.
It is a local code search tool for AI coding agents, supporting hybrid search and SQL aggregation over a repo index.
It is mainly for developers or AI coding agents that need to quickly locate implementations, gather context, and perform code statistics in a local repository.
The provided information does not include installation or configuration details; see the source repository.
Offline code retrieval and structural analysis tools for AI agents with fewer tokens.
Index local repositories into a queryable graph for AI code understanding.
Provide AI coding agents a persistent, queryable local model of repositories.
Index codebases and return query-relevant context packages for AI agents.
Turn any LLM into a codebase expert with search, mapping, and security insights.
Search and navigate multiple code repositories with natural language understanding.