Today, we’re excited to introduce the Select Star’s Model Context Protocol (MCP) server, a new capability that allows AI models and assistants to integrate directly with the Select Star data catalog, enabling context-aware data analysis and data workloads for AI agents. Select Star’s MCP server provides a secure and structured way for AI agents to query metadata, understand data lineage, and interact with datasets as part of larger data workflows.
What is Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an emerging open standard that defines how AI agents can interact with structured data systems, including databases, catalogs, and business intelligence tools. Originally introduced to address the growing integration need for context in large language model (LLM) applications, MCP creates a consistent and extensible interface between data systems and AI. By implementing an MCP Server, Select Star now allows AI tools like Cursor, or Claude Code, to fetch metadata, discover tables, trace lineage, and surface contextual explanations based on the data’s structure, usage, and governance.
How MCP Enhances AI Understanding of Your Data
With this release, users can expect AI agents to operate with a deeper understanding of their organization’s data landscape. Rather than responding to prompts based solely on public knowledge or surface-level cues, AI assistants can now ask questions such as “What is the right dataset to analyze customer churns?” or “What would get impacted if column customer_id is deleted?” and receive grounded, accurate answers directly from Select Star. This enables significantly more relevant and trustworthy outcomes, especially for enterprise use cases where metadata accuracy and compliance matter.
Since Select Star continuously ingests and updates metadata directly from source systems, you can ensure that the catalog reflects the current state of the data environment. In addition to automatic metadata synchronization, Select Star runs ongoing usage analysis that captures table and column-level popularity, tracks lineage across transformation layers, and applies semantic modeling to enhance data interpretation. This combination of freshness, usage context, and semantic enrichment gives AI agents a more accurate and complete view of the data ecosystem, allowing them to surface insights that are not only relevant but also grounded in how data is actually used and governed within the organization.
Why Data Context Matters
When AI agents operate with full awareness of your metadata, they produce more accurate and useful responses, which reduces time spent on redundant questions and manual exploration. This increases efficiency for data teams and enables faster onboarding for new analysts and engineers. More importantly, it reduces the risk of incorrect assumptions or misinterpretations, which can have downstream impacts on decision-making, compliance, or customer reporting. With MCP, AI outputs become explainable and traceable, improving trust and accountability in automated systems.
Get Started with the Select Star MCP Server
To get started with the MCP Server, visit our MCP Server documentation. Whether you're building an internal data assistant, fixing a dbt model, or automating ETL pipelines, Select Star’s MCP Server provides the structured context that AI tools need to perform effectively. By aligning AI capabilities with comprehensive metadata and lineage, Select Star helps organizations move beyond generic responses and toward deeper, more reliable insights. As the demand for trustworthy AI continues to grow, integrating metadata that you can trust will be essential for driving accurate and scalable outcomes across your data workloads.
Frequently Asked Questions
What is Select Star MCP Server?
MCP Server is a new capability from Select Star that allows AI tools like Cursor or Claude Code to securely access and interact with metadata from your data catalog.
What does MCP stand for?
MCP stands for Model Context Protocol, an open standard for integrating AI systems with structured data tools like catalogs, databases, and BI platforms.
What kinds of data tasks can AI perform with the MCP Server?
They can fetch metadata, understand lineage, discover datasets, and generate context-aware answers grounded in your organization’s data environment.
Why should I use MCP in my data workflows?
MCP reduces manual exploration, improves trust in AI outputs, and boosts efficiency by aligning AI with up-to-date metadata, usage stats, and governance details.
Where can I learn more or set it up?
You can get started by visiting the Select Star MCP Server documentation.
