KL MCP Architecture: Precision Data Management with Azure AI
Smart design delivers insights from complex datasets
By Constantine Vassilev. April 2025
Knowledge Library MCP (KL MCP) showcases the power of advanced tech paired with practical design. Built on Azure AI Agent Service and inspired by Anthropic’s Model Context Protocol (MCP), this system tackles the persistent challenge of disorganized data—think ML files, SEC filings like Tesla’s 10-Ks, workflows, and charts. The architecture behind KL MCP reveals a modular, efficient framework that processes diverse data types and delivers chat-based insights with precision. Here’s how it works, and why it matters for finance and IT professionals.
A Modular Foundation for Data Mastery
KL MCP’s architecture is a layered system, each component honed for a specific role. At its core, the integration layer—powered by Azure AI’s Knowledge API—connects user queries to specialized agents and data stores. Vector Service transforms text, tables, and images into searchable vector data, while Document Service handles raw files like PDFs. Agent Service orchestrates the workflow, ensuring queries hit the right target, whether it’s a financial filing or a workflow diagram. This setup supports up to 10,000 files, making scalability a non-issue.
User Interaction: Seamless and Intuitive
Users engage through a browser-based interface, supported by FluentUI for a polished chat experience. Speech-to-text and text input options cater to varied preferences—whether typing a query for Tesla’s 10-K or speaking it during a meeting. The Model Context Protocol (MCP) ensures context-aware responses, so a request for “filing trends” pulls relevant data, not just keywords. This layer sets the stage for efficiency, letting professionals focus on insights, not navigation.
Specialized Agents: The Heart of Precision
Within the Azure AI Foundry, KL MCP deploys custom agents for targeted tasks. TSLA 10-K Agent extracts data from Tesla’s annual reports, while TSLA 10-K PDF Agent uses OCR to process PDFs, and TSLA 10-K Table Agent handles financial tables. MCP Azure Agent oversees context integration, ensuring coherence across queries. Additional components like go-mcp-metasearch and go-mcp-brave extend search capabilities, pulling in web data for broader context. These agents embody KL MCP’s bot-driven approach—think DocBot for workflows, SECBot for filings—delivering pinpoint accuracy where broader systems falter.
Multi-Modal Data Handling: Unified Insights
KL MCP excels at multi-modal integration. Vector stores manage text, images, and tables, while image APIs process visuals via OCR and labeling. Web search APIs bring in external data, like market updates, ensuring insights stay current. This unified approach means a single chat can blend text from an SEC filing, a table from Excel, and a real-time stock price—streamlining financial reporting without juggling tools. For finance pros, this translates to faster, clearer insights for audits or trend analysis.
Backend Stability: Built to Last
The web frontend ensures operational reliability. API Service manages requests, Orchestration coordinates tasks, and Resilience Patterns keep the system stable under load. Health Monitoring tracks performance, flagging issues before they disrupt workflows. This backend design ensures KL MCP isn’t just smart—it’s dependable, a must for professionals relying on it for time-sensitive tasks like compliance checks.
External Applications: Broadening Impact
KL MCP extends beyond internal data management. External components like the Financial Analyst App leverage its insights for company evaluations, while Company Research pulls data for comprehensive profiles. Azure AI Multi-Agent RAG enhances generative capabilities, and real-time audio support opens doors for transcribing financial meetings. This flexibility makes KL MCP a versatile tool, supporting diverse needs from market analysis to operational research.
Data Flow: From Query to Insight
The architecture’s data flow is seamless. A user query—say, for Tesla’s 10-K trends—travels from the chat interface to the integration layer. The Knowledge API routes it to the TSLA 10-K Agent, which pulls vectorized data from storage, enriched by web APIs for context. Insights return through the chat, blending text, tables, and live data into a cohesive response. This loop ensures speed and relevance, cutting hours from manual searches.
Why It Matters
KL MCP’s architecture isn’t just a tech stack—it’s a solution. Finance professionals gain rapid access to filings, with 10-Ks and 10-Qs surfacing in seconds for audits or forecasts. IT teams streamline workflows, pulling insights from scattered charts and documents without delay. The system’s advanced design—leveraging Azure AI, custom agents, and vectorized storage—delivers efficiency where it counts. It’s calculated engineering for real-world challenges, not empty promises.