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Build a searchable RAG knowledge base from notes and transcripts as MCP tools.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "deeplore" yet — see the docs or source repo.
Search the deeplore knowledge base for the 'customer interview platform' project and summarize product goals, key decisions, and risks mentioned in the latest meeting.
A concise project context summary based on notes and transcripts, including goals, decisions, and risks.
Use deeplore to find transcripts from last week's engineering syncs and extract requirements, owners, and deadlines related to the API refactor.
A structured list of API refactor requirements, owners, and deadlines for follow-up.
Call the 'sales training project' tool in deeplore and provide the core project context so I can answer team questions about scope and milestones.
Project knowledge context tailored for Q&A, enabling faster and more accurate responses.
Run deep research with multiple LLMs via SSE API and MCP servers.
Enable AI to extract text, analyze structure, and understand documents deeply.
Turn unstructured documents into a searchable knowledge base for AI agents.
Unified knowledge retrieval for structured local and external information access.
Automate DeepSeek for chat, code review, summarization, and debugging assistance.
Conduct iterative deep web research, parsing, and synthesis through MCP clients.