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kosminus/querywise-mcp

by Various

An MCP server (and a CLI) that lets an LLM query your databases in natural language through a business semantic layer — glossary, metric definitions, data dictionary, knowledge bas

K

MCP

kosminus/querywise-mcp

Added 1 June 2026

#agentic-ai #database-mcp-server #mcp #mcp-server #model-context-protocol #ollama #semantic-search #sql

Overview

querywise-mcp is an open-source MCP server and CLI that enables LLMs to query databases using natural language through a business semantic layer. It combines glossary terms, metric definitions, data dictionary, knowledge base, and example queries, all grounded against the actual database schema.

Best for

Best for
Developers building natural-language data query interfaces that need business context and schema grounding

Use cases

  • Integrating natural-language database queries into internal tools or chat interfaces
  • Standardizing business metrics and definitions for consistent LLM-generated SQL
  • Building a semantic layer that reduces ambiguity in data access for non-technical users

Notes

querywise-mcp is an open-source MCP server and CLI that enables LLMs to query databases using natural language through a business semantic layer. It combines glossary terms, metric definitions, data dictionary, knowledge base, and example queries, all grounded against the actual database schema.

1 stars on GitHub. Last updated 2026-05-25. Licensed MIT.

Use cases

  • Integrating natural-language database queries into internal tools or chat interfaces
  • Standardizing business metrics and definitions for consistent LLM-generated SQL
  • Building a semantic layer that reduces ambiguity in data access for non-technical users

Pros

  • Grounds queries in real schema and business definitions, reducing hallucination and SQL errors
  • Open-source and modular, allowing customization for different databases and LLMs
  • Provides a structured way to reuse example queries and domain knowledge

Cons

  • Early-stage project with low community adoption, limited documentation and support
  • Requires upfront effort to define and maintain the semantic layer (glossary, dictionary, examples)
  • Performance and accuracy depend heavily on the underlying LLM and database complexity

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Grounds queries in real schema and business definitions, reducing hallucination and SQL errors
  • Open-source and modular, allowing customization for different databases and LLMs
  • Provides a structured way to reuse example queries and domain knowledge

Cons

  • Early-stage project with low community adoption, limited documentation and support
  • Requires upfront effort to define and maintain the semantic layer (glossary, dictionary, examples)
  • Performance and accuracy depend heavily on the underlying LLM and database complexity
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