Leena AI: Unlock Interoperability with 1000+ Agents via APIs, MCP & A2A

Introducing
Agent to Agent InteroperabilityUnlock seamless integration between your in-house Agent and Leena AI Agents. Our new APIs and Model Context Protocol (MCP) empower enterprises and developers to integrate and invoke Leena AI’s 1000+ application agents.

Trusted by global Fortune 500 enterprises

Three powerful ways to connect

Model Context Protocol (MCP)MCP allows Leena AI’s agents to work alongside your in-house agents.

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Leena AI APIsIf your conversational AI platform of choice doesn’t support MCPs.

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A2A FrameworkLeena AI supports the A2A framework for agent interoperability.

Coming Soon

Who is this for?

Enterprises building in-house assistantsAn in-house agent built on platforms like Co-Pilot Studio, Now Assist, and Agentforce can access 1,000+ integrations via Leena AI’s MCP server/API.

System Integrators (SIs)SIs building for enterprises can leverage the Leena AI MCP server/API to deliver agents across 1000+ applications in hours instead of months.

Independent Software Vendors (ISVs)ISVs who want to extend their own agents’ capabilities to orchestrate tasks across 1000+ enterprise applications can integrate with Leena AI MCP server/API.

Why it matters

True interoperabilityDon’t rebuild — connect. Keep your frontend, upgrade your backend with Leena AI.

Accelerated automationEnhance your assistant with Leena AI’s out-of-the-box IT, HR, and Finance integrations.

Unified employee experienceGet consistent, intelligent support whether your employees talk to Leena or your app.

Key capabilities

Two-way agent communicationLet your assistants and Leena AI talk — share context, invoke tasks, and deliver outcomes.

Agent invocation & handoffTrigger Leena AI’s agents to resolve HR/IT/Finance actions, or receive task handoffs from them.

Built for developers & platformsUse MCP servers or RESTful APIs to plug Leena AI into any stack.

Enterprise-readySecure authentication, fine-grained permissions, and full compliance built in.

Access control layerDeliver strict access control across all enterprise app integrations.

Transparency dashboardA comprehensive set of tools to deliver AI transparency and explainability.

Seamless integrations for enhanced functionality

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The highest trust and security standards

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Frequently asked questions

Have a question that is not answered? You can contact us at support@leena.ai

What is Model Context Protocol (MCP) in AI?

The Model Context Protocol (MCP) is a framework designed to optimize AI models by improving their context awareness. MCP allows AI models to adapt more effectively to different contexts, making them highly adaptable in various AI-driven applications. It helps AI models operate more cohesively in real-world scenarios involving multiple inputs and dynamic conditions. Model Context Protocol allows your AI to securely interact with Tools. Leena AI MCP support enables you to empower enterprises and developers to integrate and invoke Leena AI’s 1000+ application agents.

How does Agent-to-Agent (A2A) interoperability work in AI?

Agent-to-Agent (A2A) interoperability is a protocol that enables seamless communication between different AI agents. A2A allows multiple AI systems or agents to collaborate, exchange information, and collaborate efficiently. This enhances the overall functionality of AI networks, especially in complex, decentralized environments.

MCP vs A2A: Which protocol is better for AI agents?

The choice between MCP and A2A depends on your specific use case. MCP focuses on enhancing the internal context-awareness of AI models, optimizing performance within a single system. A2A, on the other hand, is designed for enhancing communication between different AI agents. If your application requires smooth agent interaction, A2A is ideal. If your focus is on optimizing individual AI models, MCP is the better choice.

What are the key differences between MCP and A2A in AI?

MCP focuses on improving context-awareness within AI models, making them more adaptable in dynamic environments. A2A, however, is all about enabling different AI agents to interact and share data. MCP optimizes single-agent performance, while A2A facilitates communication across multiple agents, enabling collaboration in decentralized systems.

Why is interoperability important in AI systems?

Interoperability is crucial in AI because it allows different AI agents, systems, and platforms to work together effectively. In a world of diverse AI technologies, ensuring smooth communication and data exchange between systems can improve efficiency, reduce friction, and enable more complex, scalable AI applications.

How does MCP enhance the performance of AI agents?

MCP enhances AI agents by improving their context-awareness, allowing them to understand and adapt to various inputs and environments. By enabling AI systems to consider a broader range of contextual factors, MCP helps optimize decision-making, prediction accuracy, and overall efficiency in AI-driven applications.

Can MCP and A2A be used together in an AI system?

Yes, MCP and A2A can be used together in AI systems to create a powerful, interoperable network of agents. While MCP improves the performance of individual AI models through enhanced context-awareness, A2A enables smooth communication between different agents. Together, they can create a more efficient and adaptive AI ecosystem.

What are some real-world applications of MCP and A2A?

MCP and A2A have a wide range of applications in industries like healthcare, finance, and autonomous vehicles. MCP is used to optimize AI systems for tasks like medical diagnostics or financial predictions, while A2A can enable collaboration between AI agents in fields like supply chain management or autonomous vehicle fleets, where seamless communication is essential.

What role does agentic AI play in Model Context Protocol and A2A?

Agentic AI refers to AI systems capable of independent decision-making and action. Both MCP and A2A are essential in enhancing agentic AI. MCP improves the context awareness of these agents, allowing them to make more informed decisions based on the situation at hand. A2A, on the other hand, enables agentic AI systems to collaborate and exchange information, enhancing their collective problem-solving abilities.

How do MCP and A2A improve AI collaboration across industries?

MCP and A2A facilitate better collaboration by allowing AI models to be more adaptable (through context-awareness) and enabling seamless communication between agents (through interoperability). These protocols enable AI systems to cooperate effectively across various industries, from healthcare to manufacturing, where multiple AI agents must work in concert to solve complex problems and optimize workflows.

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