Executive Summary
The Model Context Protocol (MCP) has emerged as a transformative standard in the realm of Robotic Process Automation (RPA), enabling seamless integration between AI capabilities and enterprise automation systems. This comprehensive article explores how MCP serves as a universal connector between RPA platforms like UiPath and Automation Anywhere and external data sources, applications, and AI services.
We examine the substantial value proposition of MCP implementation, including enhanced interoperability, reduced development overhead, and advanced intelligence capabilities. The article provides practical guidance on implementation strategies, selection criteria for development libraries, and best practices for deployment in production environments.
By leveraging MCP, organizations can unlock new levels of automation sophistication, creating more adaptive, intelligent, and efficient business processes that leverage both traditional RPA strengths and cutting-edge AI capabilities.
Understanding Model Context Protocol (MCP) Fundamentals
The Model Context Protocol (MCP) is an open standard protocol designed to facilitate seamless communication between AI systems and external data sources, applications, and tools. Introduced by Anthropic in late 2024, MCP addresses a critical challenge in enterprise automation: the fragmented integration landscape where each application or service requires custom connectors and APIs .
Think of MCP as the “USB-C of AI integration” – a universal standard that allows any AI system or automation platform to connect with any supported service through a standardized interface .
“Think of MCP Servers like having a central ‘helper team’ inside your automation setup. Instead of each robot recreating its own rules and logic, they call the helper. So when business rules change, you change them once. You reduce mistakes, speed things up, and save maintenance time. Over time that adds up to big cost savings, faster delivery, fewer errors, better control.”
MCP operates on a client-server architecture consisting of three core components:
- the MCP Host (where the AI model resides),
- the MCP Client (which handles communication),
This architecture enables AI systems to access real-time data, perform actions in external systems, and retrieve contextual information without requiring custom integrations for each service. The protocol uses JSON-RPC 2.0 for communication, providing a lightweight, language-agnostic method for remote procedure calls that is both human-readable and machine-parsable .
The protocol standardization brought by MCP is particularly valuable for enterprise automation environments where connectivity complexity has traditionally been a significant barrier to scaling AI initiatives. By providing a consistent framework for integrations, MCP reduces the development overhead associated with connecting AI systems to various enterprise resources while maintaining security and governance standards .
The Role of MCP in Enhancing RPA Platforms
MCP servers bring significant value to Robotic Process Automation (RPA) platforms by bridging the gap between traditional task automation and advanced AI capabilities. Leading RPA vendors have embraced MCP as a standard integration protocol to enhance their platforms’ intelligence and interoperability:
UiPath MCP Integration
UiPath offers comprehensive MCP support through its Orchestrator platform, allowing users to build or integrate MCP servers directly into their automation workflows . The platform supports four types of MCP servers: UiPath (exposing UiPath artifacts as tools), Coded (hosting custom-coded servers), Command (integrating external servers via package feeds), and Remote (connecting to remotely hosted servers via secure tunneling) .
This flexibility enables UiPath developers to extend automation capabilities by connecting AI models to UiPath workflows, agents, API workflows, and agentic processes through a standardized interface.
Read More on: Building MCP Server With UiPath
Automation Anywhere’s APA System
Automation Anywhere has incorporated MCP support into its Agentic Process Automation (APA) system, combining cognitive AI agents with deterministic automation on a single enterprise-grade platform . The platform uses MCP alongside other emerging standards like Google’s Agent-to-Agent (A2A) protocol to enable secure coordination across diverse agent ecosystems . This approach allows Automation Anywhere customers to design, execute, and manage intricate workflows that connect both internal and external agents, including those built on leading AI platforms like AWS Bedrock, Google Agentspace, Microsoft CoPilot, and Salesforce Agentforce
What Value Do MCP-Servers (Custom Tool Extensions) Bring to RPA?
Before the stories, here are the main kinds of value:
- Reuse & modularity: instead of duplicating logic across many bots/processes, build a central tool.
- Faster development & maintenance: changes made once in the “tool” reflect everywhere.
- Better integration capability: connect to systems or APIs outside what the RPA tool supports out-of-box.
- Scalability & performance: heavy tasks (e.g. data processing, ML inference) can be handled by a specialized service rather than the bot doing everything.
- Consistency, governance & auditability: a tool can enforce standard behavior, error handling, logging, security.
- Extendibility & adaptability: when business rules change, easier to update one place rather than many bots.
Examples Across Domains: What MCP Servers Would Enable
Here are imagined or semi-real examples in various business domains, illustrating what MCP-style tools would allow, and how they add value. Some draw on published RPA case studies to show real savings + issues.
1. Banking / Financial Services
Scenario: A bank has dozens of bots that process customer loan applications. Part of the process includes credit risk scoring, document validation, fraud detection, etc. Some bots currently rely on external APIs, others do local rules-based scripts embedded in each bot; many share overlapping logic.
How an MCP Server helps:
- Build a centralized CreditRisk Scoring Tool (an MCP Server): hosts models, handles authentication, logging, versioning of the model. All bots simply call it (send applicant data, get back risk score).
- Build a Document Validation Tool: checks if scanned documents meet quality thresholds (legibility, required fields), perhaps uses OCR + model, returns pass/fail or suggestions.
Value:
- If rules/models are updated (new regulation, changed fraud thresholds), update once centrally rather than on many bots.
- Better audit trail (who called, with what version, what output).
- Reduce redundant development effort.
- Possibly improved performance if tool can be optimized / scaled separately.
Support from published cases:
- Companies like Valenta using UiPath have shifted from pure bots to more AI-powered automation; part of that includes adding specialized components & tools rather than embedding everything into bots. UiPath
- MAS Holdings (manufacturing, but finance side included) saved thousands of labor days from accuracy and timely PO creation—having centralized, stable components would amplify such savings. UiPath
2. Healthcare / Insurance
Scenario: A health insurance company has bots for claims processing. Tasks include extracting data from claim forms (PDFs), validating against policies, detecting missing info, calculating reimbursement.
How an MCP Server helps:
- A tool for Document Extract + Validation: uses OCR + ML/NLP to pull data, check consistency, flag missing data.
- A Policy Rule Engine: centralized business rules for “what conditions are covered”, “thresholds”, etc. Bots call the engine rather than having logic embedded.
Value:
- Higher accuracy (fewer claim rejections due to wrong validation).
- Faster process: especially in cases with high volume of documents.
- Change of policy/rules can be done centrally (regulators change rules, insurer updates them) without touching many bots.
- Provides audit logs which are important for compliance in healthcare/insurance.
3. Retail / Supply Chain / Logistics
Scenario: A large retailer deals with thousands of supplier invoices, purchase orders, product data feeds. Bots reconcile supplier price changes, shipments, product catalog updates, regulatory compliance (e.g. safety labels), etc.
How an MCP Server helps:
- A Catalog Data Normalization Tool: receives raw supplier catalog feed, standardizes fields, applies normalization (units, naming conventions), returns clean data.
- A Price Change Detector: receives new price feed + old prices, flags anomalies, triggers alerts.
Value:
- Reduces errors when inconsistent product descriptions or units cause downstream issues (wrong stock levels, mislabelling).
- Faster onboarding of new suppliers/feed providers because you map them once in tool.
- Less human rework.
Published case: MAS Holdings’ use of UiPath for PO creation etc. when POs going out on time avoids delays, idle capacity, productivity loss. Having consistent data and tools for parts of that process is critical. UiPath
4. Human Resources / Shared Services
Scenario: In large enterprise, HR shared-services handles many repetitive tasks: leave approvals, payroll corrections, travel expense reimbursement, employee data updates.
How an MCP Server helps:
- A Travel/Expense Validator Tool: central checks for policy compliance (e.g. expense limits, required documentation), perhaps integrates with other systems (credit card data).
- A Onboarding Data Verifier: checks whether employee data submitted matches identity records, background check results, etc.
Value:
- Reduced errors and back-and-forth, faster processing of employee requests.
- Consistency in policy application (e.g. always same thresholds).
- HR bots across departments can use same tools → lowers maintenance cost.
Why MCP Server is Particularly Useful (vs just building reusable libraries in bots)
MCP Servers give extra advantages beyond just “write reusable code inside bots”:
- They are managed/hosted as separate services (or processes) — so versioning, deployment, and scaling are decoupled from each bot.
- Orchestrator (or equivalent in other RPA tools) sees and manages them as part of the automation stack — better observability, security, permissions.
- They can be external/remote, or coded tools; so you can integrate components using different languages/technologies than the bot platform might directly support.
Possible Drawbacks / Things to Watch
To be fair, there are challenges:
- Upfront cost & complexity in building a shared tool vs small bot specific code.
- You need good governance: versioning, handling backward compatibility.
- If tool has bugs, many processes depend on it, so impact is large.
- Performance & availability concerns: the tool must scale and be reliable.
MCP Servers bring transformative value to RPA workflows by acting as a universal bridge between traditional automation and AI-powered capabilities. Through standardized interoperability, enhanced intelligence, and seamless connectivity, they enable organizations to automate increasingly complex processes across various business domains including HR, sales, healthcare, and IT support.
The implementation of MCP Servers with leading RPA platforms like UiPath and Automation Anywhere follows a structured approach involving server creation, tool configuration, authentication setup, and client configuration. By following best practices and selecting appropriate tools based on technical compatibility and management needs, organizations can maximize the value of their automation investments.
As the technology continues to evolve, MCP-RPA integration will play a crucial role in the journey toward hyperautomation, enabling organizations to achieve unprecedented levels of efficiency, adaptability, and intelligence in their business processes. The organizations that strategically adopt and implement this technology today will gain significant competitive advantages in the increasingly automated business landscape of tomorrow.