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Index codebases and documents for fast semantic search via MCP.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ollqd" yet — see the docs or source repo.
Index the current project and search for implementations related to "user login authentication middleware". Return the most relevant files, code snippets, and explanations.
A list of matching code files, key snippets, and explanations to quickly locate the implementation.
Index the docs directory and find documentation related to "how to configure vector database connections". Summarize the results ranked by relevance.
Relevant document passages, source paths, and a short summary for quickly understanding the configuration steps.
Index both the codebase and project documents, then search for content related to "embedding model selection". List usage locations in code and explanations in docs separately.
Separate results for code and documentation, helping connect implementation details with written guidance.
Query and manage LlamaIndex documents stored in Qdrant vector databases.
Connect Ollama to MCP clients for real-time web search and content fetching.
Search Oli/LimX docs and return relevant answers with citations.
Connect local Ollama models to MCP clients for discovery and Q&A.
Offload simple coding tasks to local Ollama and reduce Claude API usage.
Run Qdrant for vector storage and semantic search with MCP integration.