Introducing MCP Client and Server Applications for Turning LLMs into Domain Experts

A common challenge emerges as organizations rush to leverage large language models for everything from customer support to complex data analysis. Generic LLMs often hallucinate or produce inconsistent outputs when faced with niche, domain-specific tasks. 

The solution? Layer domain expertise on top of foundational models by packaging pre-trained AI applications, transforming a generalist LLM into a true specialist.

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MCP at the Core of Domain Wrapping

Model context protocol isn’t just a transport layer. It’s the orchestration fabric that:

• Discovers available modules (Resources, Tools, Prompts) in a catalog.
• Invokes the right pre-trained application via a JSON-RPC call.
• Streams validated and structured context in your LLM prompt.
• Logs every step for auditability and compliance.

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A Library of 100+ Pre-Trained Domain Modules

Each module encapsulates domain logic, data validation, and best practices. Examples include:

Finance & Compliance

• Bank Statement Analyzer: Parses, categorizes, and flags anomalies.
• Invoice Processor: Extracts line-item details and matches against purchase orders.
• Risk-Scoring Engine: Calculates credit or fraud risk based on configurable rules.

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Privacy & Security

• PII Masking: Locates and redacts personally identifiable information in documents. 
• Liveness Detection: Validates live face captures versus static images.
• Deepfake Detector: Analyzes multimedia for synthetic manipulation artifacts

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Insights & Automation

• Sentiment Analysis: Scores customer feedback across channels.
• Invoice Processor: Automates extraction and validation of billing details.
• Signature Verifier: Confirms the authenticity of handwritten or digital signatures against known templates.

And over 100 more modules spanning healthcare, legal, HR, marketing, and beyond.

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How MCP Client & Server Enable Rapid Assembly

1. Module Discovery: Arya’s Apex queries the MCP Server for the catalog of all AI modules.
2. Context Pre-Processing: Selected module ingests raw input (PDF, image, text) and outputs sanitized JSON.
3. LLM Invocation: MCP Client wraps the JSON context into a prompt, routing it to your LLM of choice.
4. Response Validation: Post-processing modules (if any) can enforce domain rules on generated text before delivery.
5. Traceability: Every module call and LLM interaction is logged with timestamps and metadata.

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Key Benefits of MCP-Driven Specialization

• Reduced Hallucinations: Clean, module-validated context keeps LLM outputs grounded.
• Plug-and-Play Agility: Swap modules or add new ones without touching your core application.
• End-to-End Audit: Full visibility into data transformations and generative steps for compliance.
• Scalable Composition: Chain multiple modules (e.g., redact PII → analyze sentiment → summarize) in a single workflow.

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Real-World Scenarios

• Banking: In one flow, extract data, parse transactions, score risk, and generate an executive summary—all via MCP.
• Reg-Tech: Automate regulatory compliance workflows, feeding validated data into audit and reporting systems.‍
• Customer Experience: Analyze sentiment on support tickets, auto-classify issues, and generate follow-up recommendations seamlessly.

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Getting Started with MCP & Apex

1. Explore the Module Catalog on Arya.ai’s Apex platform. 
2. Spin Up a Sandbox: Test key modules (Bank Statement Analyzer, Aadhar Masking, Deepfake Detector) against your data.
3. Connect Your LLM: In Apex's MCP Client settings, configure your preference (OpenAI, Anthropic, Azure, on-prem).
4. Compose Workflows: Use Apex’s UI to select and chain modules for end-to-end domain-wrapped AI applications.

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With MCP Client & Server at its heart, Apex turns any generic LLM into a verifiable domain expert—accelerating time-to-value, minimizing risk, and unlocking truly trustworthy AI at scale.

If you’d like to learn more, Connect with us.