Overview of the AI Project Maya MCP Server

The AI Project Maya MCP server is a lightweight, purpose‑built gateway that enables AI assistants to interact seamlessly with a custom data repository and workflow tooling. By exposing a set of well‑defined resources, tools, and prompts through the Model Context Protocol, it removes the friction that typically surrounds data ingestion, transformation, and execution in AI‑driven projects. Developers can therefore focus on building high‑value logic while the server handles protocol compliance, security, and state management.

At its core, Maya provides a data‑centric API that allows an AI assistant to query, update, and retrieve structured information stored in a local or cloud‑based database. The server automatically translates MCP requests into SQL (or NoSQL) queries, returning results in a format that the assistant can consume directly. This eliminates the need for custom adapters or manual data parsing, making it trivial to add new datasets or modify existing schemas without changing the assistant’s code.

Key features include:

• Resource Registry – A declarative catalog of data tables, views, and external APIs that the assistant can access. Each resource is annotated with permissions, data types, and validation rules.
• Tool Integration – Built‑in tooling for common operations such as data cleaning, aggregation, and statistical analysis. These tools are exposed as callable actions that the assistant can invoke with a single line of natural language.
• Prompt Templates – A library of reusable prompt patterns that embed contextual data automatically, ensuring consistent and accurate responses from the assistant.
• Sampling Control – Fine‑grained sampling settings that allow developers to dictate how much data is returned, improving performance and cost for large datasets.

Real‑world use cases span from automated reporting—where an assistant pulls quarterly sales figures, runs a quick aggregation tool, and formats the output into a Markdown report—to dynamic data‑driven recommendations, such as suggesting inventory reorder levels based on current stock and historical trends. In research environments, Maya can serve as a sandbox for data scientists to prototype new models and immediately test them against live data through the assistant interface.

Integrating Maya into an AI workflow is straightforward: developers expose the server as a MCP endpoint, configure the assistant’s toolset to include Maya’s resources and tools, and then craft prompts that reference those capabilities. The assistant handles the rest—making authenticated calls, parsing responses, and maintaining conversational context—so developers can iterate rapidly without wrestling with low‑level networking or authentication details.

What sets Maya apart is its single‑source‑of‑truth approach. By centralizing data access and tool execution behind the MCP, it guarantees consistency across all assistant interactions, reduces duplication of logic, and provides a clear audit trail of queries and operations. For teams that rely on AI assistants to surface insights from complex datasets, Maya offers a robust, protocol‑compliant foundation that scales with the size and complexity of their data ecosystem.