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Index codebases into Qdrant for semantic code search and faster AI retrieval.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "qdrant-codebase-mcp" yet — see the docs or source repo.
Index this Git repository into Qdrant and explain which directories and file types were indexed, plus how to search the code later using natural language.
A summary of the indexing result, including coverage, processing status, and instructions for semantic search.
Search the indexed codebase for code related to 'retry and lockout logic after failed user login' and list the most relevant files, functions, and reasons.
A relevance-ranked list of code locations with brief explanations of why each matches the query.
Using the existing Qdrant index, quickly identify modules, dependencies, and key entry files related to the payment flow in this project.
A concise code map of the payment flow to help quickly understand the project structure and key implementations.
Build semantic indexes for codebases to find relevant code with natural language queries.
Search and navigate multiple code repositories with natural language understanding.
Query and manage LlamaIndex documents stored in Qdrant vector databases.
Use Qdrant via MCP for vector search, collection management, and semantic retrieval.
Run Qdrant for vector storage and semantic search with MCP integration.
Index repositories into a persistent graph for fast code search and understanding.