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1 month ago
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Agentic RAG with MCP Server
The MCP server provides tools for entity extraction, query refinement, and relevance checking to enhance RAG applications by integrating with OpenAI and Gemini.
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// key findings
✅
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
🔐
You'll be asked for 3 credentials: MONGODB_PASSWORD, PINECONE_API_KEY, OPENAI_API_KEY
These are read from process.env at runtime. Make sure you trust where they’ll be sent.
// required environment variables
This server reads these from
process.env. You'll be asked to provide them before it can run.config
MONGODB_USERNAME🔐 secret
MONGODB_PASSWORDconfig
MONGODB_URI🔐 secret
PINECONE_API_KEYconfig
PINECONE_INDEX_NAMEconfig
PINECONE_CLOUDconfig
PINECONE_REGION🔐 secret
OPENAI_API_KEYconfig
OPENAI_MODEL_NAME— "your-model-name-here"// full audit trail
The full breakdown of what we checked, the deductions that landed, the network hosts, the dependency advisories, and concrete fix guidance is available to verified publishers.
// improvement guidance — verified publishers only
We have 6 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
[](https://m8ven.ai/mcp/iflow-mcp-agentic-rag-with-mcp-server-1-t5x1d4)commit: 6836d4b666887357aa18aad4fdf7fc076d2c8a68
code hash: 68f4eefd92f22950d5308035402c00bb420010eadb2de7226e16fc559609e44d
verified: 6/16/2026, 1:27:52 PM