$ loading_
Manage vector stores, files, and semantic search for knowledge retrieval workflows.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "@dotlab-hq/vector-store-mcp" yet — see the docs or source repo.
Create a new OpenAI vector store named "Product Docs" for my project, batch upload the PDF and Markdown files from the docs folder, and return the vector store ID, uploaded file list, and processing status.
Returns the new vector store details, upload results, batch status, and identifiers for later search.
Search the "Product Docs" vector store for content most relevant to "API rate limits and retry strategy", and return the top 5 matches with filenames, snippet summaries, and relevance notes.
Outputs a ranked list of relevant document snippets to quickly locate key information in the knowledge base.
Check the file status in the specified vector store, list any failed or unfinished files, retry batch processing for them, delete duplicate older uploads, and summarize the final results.
Returns the file inventory, problematic items, retry and cleanup results, and the updated vector store status.
Connect AI agents to secure RAG workflows across multiple vector databases.
Store typed customer records and query them with exact and semantic search.
Adds nodes, edges, and semantic retrieval to agent knowledge graphs.
Search local markdown vector stores with LLMs and evaluate retrieval quality.
Manage dotfiles repos, syncing, tracking, and remotes through natural language.
Lightweight vector memory for AI agents to store, search, and delete memories.