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
MCP
kosminus/querywise-mcp
Added 1 June 2026
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
Pairs with
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