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Systematically research, capture, and organize sourced knowledge with a graph-based MCP tool.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "nicktools" yet — see the docs or source repo.
Research “AI agents in enterprise customer service,” capture web sources from the past year, preserve source links and key details, build a knowledge graph by themes, claims, and evidence, and summarize the main conclusions and debates.
A sourced research output with a knowledge graph structure, reference links, key findings, and a summary of debates.
Collect articles, reports, and blog posts about “declining inference costs of open-source LLMs,” label every source, compare differences in conclusions, and identify which claims have stronger cross-source support.
A multi-source comparison including a source list, credibility assessment, cross-validation results, and a conclusion summary.
Capture relevant webpages for this topic, extract people, organizations, events, and dates, build entity relationships, and output structured research notes for further analysis.
Structured research notes and entity relationships that support further search, analysis, and source citation.
Turn codebases into structural graphs for efficient AI-assisted code exploration.
Secure internal knowledge retrieval with permission-aware access control and citation enforcement.
Adds nodes, edges, and semantic retrieval to agent knowledge graphs.
Generate, search, and reason over knowledge graphs from data sources without API keys.
Ingest documents into Neo4j to build and query a knowledge graph.
Centralize knowledge, run semantic search, ingest documents, and generate RAG answers.