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Search indexed codebases semantically with natural language across MCP clients.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "code-search" yet — see the docs or source repo.
In the indexed code folders, find code related to user login, token validation, and session management, and list the most relevant files and functions.
A ranked list of the most relevant files, functions, or snippets for the authentication flow.
Search this project for implementation code related to API retry logic, exception handling, and error logging.
Relevant code locations with context to help understand the project's error handling approach.
I want to add an export feature. Search the indexed codebase for existing implementations similar to file download, report export, or CSV generation.
Semantically similar existing implementations that can be reused as patterns.
When working in an unfamiliar or large codebase, developers can describe a concept in natural language to quickly locate relevant files and functions instead of manually browsing.
Teams maintaining searchable indexes over time can use automatic file watching so search stays aligned with code changes.
In MCP-compatible AI clients, users can issue natural-language code queries and integrate semantic search into their regular development assistant workflow.
It performs semantic code search over indexed code folders. Users can query relevant code in natural language and use it through MCP clients.
The provided information only says it supports multiple embedding providers, but does not list them. See the source repository for details.
The description mentions automatic file watching, so it can track updates when code folders change. See the source repository for exact behavior and configuration.
Search codebases semantically with natural language to find relevant files and logic.
Search and navigate multiple code repositories with natural language understanding.
Search local code with text, symbol, and semantic hybrid retrieval.
Search large monorepos with full-text and structural code queries.
Index codebases with AST awareness and retrieve code context via semantic search.
Semantically search and analyze multilingual code with AST-aware insights.