AI is getting smarter by the day, but here’s the problem: most of it is still stuck on the outside of enterprise systems, knocking on the door. Financial institutions, in particular, operate in complex environments like legacy software, siloed data, strict compliance rules. You can have the smartest model in the world, but if it can’t securely access the tools and information it needs, it’s not much help.
That’s where MCP (Model Context Protocol) comes in. It offers a standard way for AI to actually connect and interact with real-world business tools and data. Think of it like a universal adapter for AI – one that finally lets intelligent systems plug into the messy realities of enterprise infrastructure.
At Unique, we’re building AI agents specifically for financial services, and MCP has been a game-changer. But it’s not just about making connections. When paired with our agentic AI platform, MCP becomes the foundation for something much more powerful: AI that can take initiative, complete tasks, and work alongside humans as a true digital teammate.
However, it's important to note that MCP is powerful but it’s not always the best solution by itself. We aim to harness its strengths while acknowledging its limitations. We design AI agents with both clarity and pragmatism, choosing the right tool for the right task.
This article unpacks how that all works: from what MCP actually is, to how we use it to bring autonomous AI into financial workflows.
What is the Model Context Protocol (MCP)?
MCP is an open protocol introduced by Anthropic in 2024 to standardize how AI systems access external tools and data. It provides a consistent, extensible structure for integrating LLMs and agents with various digital resources using a combination of JSON-RPC 2.0, SDKs, and modular transport layers.
In simple terms, MCP enables AI models to act as clients in a client-server architecture. Servers expose functionality, whether that’s document storage, customer data, risk models, or even full-blown applications, through standardized interfaces. The result is a unified way for AI systems to query, retrieve, and interact with external resources, securely and efficiently.
MCP tackles the notorious M×N integration problem, where each AI model must be custom-integrated with every potential tool. Instead, MCP creates one standard that tools and models can both adhere to, enabling plug-and-play interoperability across the ecosystem.
MCP in Action
Since its release, MCP has seen rapid adoption across the AI landscape. Major players like OpenAI, Google DeepMind, Microsoft, and startups such as Replit and Sourcegraph have integrated MCP to enable agentic capabilities within their platforms.
Practical examples of MCP in action include:
-
Software Development: AI agents that understand project context from GitHub, edit code in Replit, and debug in real time.
-
Enterprise Use Cases: Autonomous agents that analyze contracts, retrieve compliance documentation, or assist with onboarding workflows via internal APIs.
-
Security Enhancements: Extensions like MCP Guardian introduce layered security through access control, traceability, and safe execution environments.
This sets the stage for a new kind of AI – one that doesn’t just analyze data but interacts with it to achieve real-world outcomes.
Reality Check: MCP Isn’t a Universal Fix
But every tool has boundaries.
-
Core Infrastructure Still Decides Quality
For instance, integrating Outlook via MCP lets agents create drafts or fetch messages, but Outlook’s native search is weak. Asking “summarize all emails from my boss last quarter” may fall flat; not due to MCP, but due to poor data retrieval.
-
Complex Environments and Performance Constraints
Standard MCP setups often rely on local JSON‑RPC channels, which can be incompatible with mobile, browser-based, or edge environments. Innovations like MCP Bridge, a RESTful proxy with secure execution modes, are emerging to address these limits.
-
Beyond Tool Access: Orchestration Matters
MCP enables access, but not coordination. Complex, multi-agent workflows may require additional protocols like A2A, ACP, or ANP to manage discovery, messaging, and decentralized execution
Unique’s Dual Innovation: Agentic AI Powered by MCP
At Unique, we specialize in building agentic AI for financial institutions. Our platform empowers AI agents to autonomously complete middle- and back-office tasks, such as:
-
Generating meeting summaries and investment memos
-
Performing real-time KYC and compliance validation
-
Automating due diligence across customer portfolios
-
Integrating with CRMs, internal databases, and email systems
By integrating MCP, we’ve enabled these agents to operate with improved precision. Instead of brittle, hard-coded APIs, we now use the MCP framework to plug into client systems securely and dynamically.
But we don’t default to the newest standard. We evaluate which approach delivers the best outcome:
-
If MCP is ideal, we leverage its efficiency and integration simplicity.
-
If traditional APIs or custom indexing is stronger, we’ll build that.
-
If a hybrid solution fits best, we’ll combine approaches intelligently.
Security and Governance: Responsible Autonomy
With great power comes great responsibility. As agentic AI gains operational control, security becomes non-negotiable. Unique sticks to strict governance standards, implementing consent layers, access control, user visibility, and auditability at every step.
Inspired by frameworks like MCP Guardian, we ensure every interaction is logged, every permission is explicit, and every task is reversible. Our approach blends innovation with oversight, ensuring that autonomy enhances trust, not undermines it.
What’s Next: The Future of AI Workflows
Looking forward, MCP represents the beginning of a broader evolution in AI interoperability. Emerging standards like the Agent Communication Protocol (ACP) and Agent-to-Agent Protocols (A2A) will layer on collaborative and distributed capabilities.
Our mission is clear: to empower financial professionals with a truly agentic workforce, enabled by cutting-edge protocols and secure, scalable AI.
Final Thoughts
At Unique, we’re committed to using all new technology judiciously, which means we always pair it with the right methods and full transparency. Our goal isn’t just connectivity; it's delivering AI that’s reliable, practical, and trusted.
From Connectivity to Capability: How MCP Unlocks Agentic AI
AI is getting smarter by the day, but here’s the problem: most of it is still stuck on the outside of enterprise systems, knocking on the door. Financial institutions, in particular, operate in complex environments like legacy software, siloed data, strict compliance rules. You can have the smartest model in the world, but if it can’t securely access the tools and information it needs, it’s not much help.
That’s where MCP (Model Context Protocol) comes in. It offers a standard way for AI to actually connect and interact with real-world business tools and data. Think of it like a universal adapter for AI – one that finally lets intelligent systems plug into the messy realities of enterprise infrastructure.
At Unique, we’re building AI agents specifically for financial services, and MCP has been a game-changer. But it’s not just about making connections. When paired with our agentic AI platform, MCP becomes the foundation for something much more powerful: AI that can take initiative, complete tasks, and work alongside humans as a true digital teammate.
However, it's important to note that MCP is powerful but it’s not always the best solution by itself. We aim to harness its strengths while acknowledging its limitations. We design AI agents with both clarity and pragmatism, choosing the right tool for the right task.
This article unpacks how that all works: from what MCP actually is, to how we use it to bring autonomous AI into financial workflows.
What is the Model Context Protocol (MCP)?
MCP is an open protocol introduced by Anthropic in 2024 to standardize how AI systems access external tools and data. It provides a consistent, extensible structure for integrating LLMs and agents with various digital resources using a combination of JSON-RPC 2.0, SDKs, and modular transport layers.
In simple terms, MCP enables AI models to act as clients in a client-server architecture. Servers expose functionality, whether that’s document storage, customer data, risk models, or even full-blown applications, through standardized interfaces. The result is a unified way for AI systems to query, retrieve, and interact with external resources, securely and efficiently.
MCP tackles the notorious M×N integration problem, where each AI model must be custom-integrated with every potential tool. Instead, MCP creates one standard that tools and models can both adhere to, enabling plug-and-play interoperability across the ecosystem.
MCP in Action
Since its release, MCP has seen rapid adoption across the AI landscape. Major players like OpenAI, Google DeepMind, Microsoft, and startups such as Replit and Sourcegraph have integrated MCP to enable agentic capabilities within their platforms.
Practical examples of MCP in action include:
Software Development: AI agents that understand project context from GitHub, edit code in Replit, and debug in real time.
Enterprise Use Cases: Autonomous agents that analyze contracts, retrieve compliance documentation, or assist with onboarding workflows via internal APIs.
Security Enhancements: Extensions like MCP Guardian introduce layered security through access control, traceability, and safe execution environments.
This sets the stage for a new kind of AI – one that doesn’t just analyze data but interacts with it to achieve real-world outcomes.
Reality Check: MCP Isn’t a Universal Fix
But every tool has boundaries.
Core Infrastructure Still Decides Quality
For instance, integrating Outlook via MCP lets agents create drafts or fetch messages, but Outlook’s native search is weak. Asking “summarize all emails from my boss last quarter” may fall flat; not due to MCP, but due to poor data retrieval.
Complex Environments and Performance Constraints
Standard MCP setups often rely on local JSON‑RPC channels, which can be incompatible with mobile, browser-based, or edge environments. Innovations like MCP Bridge, a RESTful proxy with secure execution modes, are emerging to address these limits.
Beyond Tool Access: Orchestration Matters
MCP enables access, but not coordination. Complex, multi-agent workflows may require additional protocols like A2A, ACP, or ANP to manage discovery, messaging, and decentralized execution
Unique’s Dual Innovation: Agentic AI Powered by MCP
At Unique, we specialize in building agentic AI for financial institutions. Our platform empowers AI agents to autonomously complete middle- and back-office tasks, such as:
Generating meeting summaries and investment memos
Performing real-time KYC and compliance validation
Automating due diligence across customer portfolios
Integrating with CRMs, internal databases, and email systems
By integrating MCP, we’ve enabled these agents to operate with improved precision. Instead of brittle, hard-coded APIs, we now use the MCP framework to plug into client systems securely and dynamically.
But we don’t default to the newest standard. We evaluate which approach delivers the best outcome:
If MCP is ideal, we leverage its efficiency and integration simplicity.
If traditional APIs or custom indexing is stronger, we’ll build that.
If a hybrid solution fits best, we’ll combine approaches intelligently.
Security and Governance: Responsible Autonomy
With great power comes great responsibility. As agentic AI gains operational control, security becomes non-negotiable. Unique sticks to strict governance standards, implementing consent layers, access control, user visibility, and auditability at every step.
Inspired by frameworks like MCP Guardian, we ensure every interaction is logged, every permission is explicit, and every task is reversible. Our approach blends innovation with oversight, ensuring that autonomy enhances trust, not undermines it.
What’s Next: The Future of AI Workflows
Looking forward, MCP represents the beginning of a broader evolution in AI interoperability. Emerging standards like the Agent Communication Protocol (ACP) and Agent-to-Agent Protocols (A2A) will layer on collaborative and distributed capabilities.
Our mission is clear: to empower financial professionals with a truly agentic workforce, enabled by cutting-edge protocols and secure, scalable AI.
Final Thoughts
At Unique, we’re committed to using all new technology judiciously, which means we always pair it with the right methods and full transparency. Our goal isn’t just connectivity; it's delivering AI that’s reliable, practical, and trusted.