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Provide LLMs a governed data layer for SQL, metrics, lineage, and catalog.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "ai.matih/mcp" MCP server from askskill: Run: claude mcp add 'ai-matih-mcp' -- npx -y @matihlabs/mcp
Use ai.matih/mcp to access the data layer, retrieve this month's active users, paid conversion rate, and the metric definitions, then explain the data semantics.
Returns metric values along with metric definitions or semantic explanations.
Use ai.matih/mcp to find which tables and fields the 'net revenue' metric depends on, and list the lineage from upstream to downstream.
Outputs related tables, fields, and lineage paths to clarify data origins.
Using ai.matih/mcp, search the data catalog for datasets, tables, or metrics related to 'order refunds' and provide a brief usage note for each.
Returns a list of matching data assets with brief descriptions.
Developers building chat assistants or analysis agents can use it to provide governed SQL, metrics, and catalog access, reducing the risk of exposing LLMs to fragmented data sources.
Data analysts can use it to inspect metric definitions, related data assets, and lineage, helping them validate semantics and provenance faster.
Product managers can search the data catalog for existing tables, metrics, and descriptions before requesting analysis, reducing back-and-forth communication.
It is a data access layer for LLMs focused on governed capabilities, including SQL, metrics, lineage, and catalog. It is suitable for supplying trusted data context to an LLM.
The provided information only describes its data capabilities and does not include installation steps, runtime, or key requirements. See the source repository for details.
Based on the description, it is more than a query connector and emphasizes governance features such as metrics, lineage, and catalog. That makes it better suited for giving LLMs structured and explainable data context.
Ground dataset questions with schema, lineage, and trust signals.
Enable AI to safely analyze business metrics without improvising raw SQL.
Discover APIs, manage categories, and run SQL queries through MCP.
Connect to Data Graphs for natural-language graph search and querying.
Safely query SQLite databases and preview schemas through a standardized MCP interface
Query structured data in natural language without needing SQL or API expertise.