Overview

The Enterprise MCP Server for ReportServer Integration is a cloud‑native Model Context Protocol (MCP) implementation that bridges AI assistants with the Java‑based ReportServer platform. It solves a common pain point for enterprises: exposing rich, context‑aware data and reporting capabilities to generative AI without compromising security or performance. By exposing ReportServer’s data, dashboards, and scripting engine through a standardized MCP interface, developers can build conversational agents that query, analyze, and even generate reports directly from a chat session.

At its core, the server offers a dual‑layer architecture. The front end is a Blazor‑powered browser workspace (RSChatApp.Web) that handles user sessions, conversation context, and UI rendering. The back end hosts an LLM provider layer that can route requests to a variety of models—Ollama, Anthropic Claude variants, OpenAI GPT‑4o, and Azure AI. Intelligent provider selection ensures that the best model is chosen based on cost, latency, or feature requirements, with graceful fallbacks. A vector database (Qdrant) supplies semantic retrieval and RAG capabilities, enabling the assistant to pull in PDFs, Markdown docs, Groovy scripts, and CLI examples from ReportServer’s asset repository.

Key capabilities include:

• Context‑aware reporting: The assistant can ask for specific metrics, slice data by dimensions, and return visualizations or raw CSVs.
• Dynamic script generation: By ingesting Groovy build scripts and automation workflows, the server can suggest or produce new scripts tailored to user needs.
• Hybrid LLM orchestration: Multiple models can be leveraged in tandem—one for natural language understanding, another for code generation—while the MCP framework orchestrates the flow.
• Session persistence options: While currently in‑memory, future releases will support topic‑based history and cross‑session context, allowing long‑running analytical conversations.

Typical use cases span enterprise analytics, operational dashboards, and self‑service BI. For example, a data analyst can ask the assistant to “show me last quarter’s sales trend by region” and receive an interactive chart generated from ReportServer, or a DevOps engineer can request a new Groovy deployment script that incorporates the latest security policies. The MCP server’s clean API surface means it can be integrated into existing AI workflows—whether a Claude chatbot, an internal knowledge base, or a custom in‑house assistant—without rewriting the underlying data access logic.

Unique advantages of this implementation are its model agnosticism and cloud‑native scalability. Built on .NET 9.0, it can run anywhere from a local Docker container to an Azure Kubernetes Service cluster, automatically scaling based on incoming query load. The intelligent routing layer not only optimizes cost but also guarantees low latency for critical reporting queries, ensuring that users experience real‑time insights without waiting for model inference to complete.