Business Analytics RAG
Business analytics and knowledge retrieval system that combines MCP servers for data analysis and RAG (Retrieval-Augmented Generation) capabilities with flexible LLM backend support for both Google Gemini and custom localhost APIs. The implementation provides two specialized MCP servers: a business analytics server that performs statistical operations like mean calculation, correlation analysis, and linear regression on CSV business data, and a RAG server that searches through business knowledge documents for terms, definitions, and company policies. Built with Python using pandas for data processing and supporting both Gemini API and custom OpenAI-compatible endpoints, it's designed for business intelligence workflows where users need to combine quantitative data analysis with contextual business knowledge retrieval through natural language interactions.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately.
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- ai-ml
- Source
- PulseMCP
- Repository
- github.com/ansh-riyal/mcp-rag
- Author type
- human
- Updated
- 2026-04-25
Use via MCP
Resolve Business Analytics RAG from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.