Dual MCP Support in Astera AI: What it is and Why it Matters

Enterprise automation didn’t start with AI agents, but they’ve had a much bigger impact than earlier automation methods, such as software scripts or bots. Modern AI agents can do a lot more than tackle repetitive tasks. They can reason through complicated workflows, choose the best course of action, and access tools to execute said action.  

But to do all this, AI agents require interoperability. They need to be able to connect to numerous tools, databases, services, and APIs. This connectivity is what turns agents into automation tools, and the Model Context Protocol (MCP) is a unified way of making it happen. 

Enterprise-grade automation demands dual MCP support — AI agents should both act and be acted upon as required for the task at hand. Astera AI’s native support for MCP client and server roles in the same platform helps you create smarter AI agents that act as peers, exchanging knowledge, services, and information with each other. 

In this article, we’ll discuss MCP’s role in enterprise AI, how MCP server and client roles vary, and the benefits of combining these roles.

Tool Calling and MCP Architecture in Enterprise AI Systems 

Tool calling and MCP are both ways to augment AI agents’ capabilities, but they differ in scope. Tool calling is what an AI model uses during a conversation to invoke predefined functions built into its inference process. The model itself decides which tool to call and when to call it, depending on the conversation. 

MCP is an open-source, standardized protocol that Anthropic introduced in late 2024 to unify how AI agents or LLMs connect with various tools and systems. MCP has been described as a “USB-C port for AI agents”, a great analogy for its universality, since it enables any AI agent to plug into any tool or system without bespoke integration. 

The two approaches are complementary. Where MCP provides the architecture to enable connectivity with external systems, tool calling is the interface an AI model uses to interact with those systems during a conversation. 

In enterprise AI systems, MCP helps AI agents draw from a wider range of data sources and proprietary knowledge bases. This is important because your AI agents need access to business data. The more high-quality, contextually relevant data sources that your agents can tap into, the better they can understand and fit into your business workflows and the more value they can deliver.  

It’s not an overstatement to say that MCP contributes directly to your AI agents’ success. 

MCP is set up as a client-server architecture: an AI host, such as a chatbot or IDE assistant, runs one or more MCP clients. Each client connects to a remote MCP server. This architecture is illustrated below:

A typical MCP client-server architecture

MCP clients are lightweight modules embedded within the AI application. A client will perform a discovery handshake with the server, note its capabilities (i.e., the tools, resources, and prompts offered by the server), and forward requests (such as data queries or function calls) from the LLM to the server. When a client calls a tool, the MCP server will execute it and return the result. 

An MCP server can either be configured using third-party integration (think a connector for Stripe or Google Calendar) or through internal infrastructure. This decoupling allows any MCP client to connect to any MCP server. 

Applications within the MCP domain have traditionally offered users a binary choice, which means any given system could either be an MCP client or an MCP server, but never both. In a client role, an application will only initiate calls to servers without being invoked as a tool itself. In a server role, a service will only expose its capabilities (tools or resources) to clients, without being able to call other MCP servers. 

However, an application can also function as both a client and a server. This dual role puts MCP at the center of complex, layered systems. We’ll examine this dual functionality and its significance in detail a little later in this blog.

MCP Server Capabilities in Astera AI

Astera AI functions as a fully capable MCP server, enabling easy integration of AI agents and services into your enterprise ecosystem. Following the MCP standard, Astera AI exposes a structured, machine-readable catalog of agents, models, and services. MCP clients can query and consume this catalog, letting organizations discover and invoke AI capabilities across their internal or partner systems.

At the server level, Astera AI ensures the secure exposure of agents and services through granular access controls and authentication protocols. You can configure which teams have access to which agents, define usage permissions, and apply role-based policies to maintain strict data governance and compliance standards.

Astera’s MCP server functionality is integrated with solutions such as ReportMiner and Data Pipeline. This means our users can easily expose and orchestrate data-related MCPs, such as data transformation flows, enrichment processes, and cleansing routines. Their downstream applications can obtain high-quality, pre-processed data in real time through standardized MCP interfaces. 

External systems, such as cloud platforms, ERP applications, or even custom-built solutions, can all function as MCP clients and consume Astera-hosted AI agents without having to host them locally. This lowers operational overhead for our partner organizations while also giving them consistent, secure access to AI-driven functionality. 

A few real-world examples of Astera AI’s MCP server functionality include automated invoice processing in AP systems, streamlining customer data enrichment for CRM platforms, and powering intent recognition engines in contact center software.

MCP Client Capabilities in Astera AI

Astera AI also functions effectively as an MCP client. This means that the AI agents developed using Astera AI can readily interact with external, MCP-compliant servers to request models, consume services, retrieve results, and initiate workflows without hardcoded individual integrations.

These client capabilities support integration with third-party data sources and business applications such as ERPs, CRMs, and cloud data warehouses, enabling MCP agents to ingest contextual data in real time. Moreover, agents can also invoke legacy systems, APIs, or proprietary tools that you’ve already deployed within your organization, making them interoperable with a modern agentic AI ecosystem. 

Real-world use cases for MCP client functionality include supply chain agents pulling demand forecasts from external ML APIs, customer service bots fetching real-time SLA data from legacy backends, or finance agents retrieving information from credit scoring services.

The Duality of MCP and Its Importance

We’ve discussed the MCP client and server roles individually and explored how they function within Astera AI. Let’s look at the dual MCP client-server role and why it matters. 

As mentioned earlier, the conventional approach for an MCP application is to work as a client or a server. This either/or approach hinders flexibility and leaves limited room for innovation and experimentation. 

More importantly, MCP’s design is deliberately composable. There are no physical distinctions between an MCP client and an MCP server. The differences between the two are merely logical. Which is why an application can simultaneously manage two different kinds of connections: outbound MCP links to upstream servers and inbound MCP connections from downstream clients. 

Dual MCP support lets any AI agent consume services from MCP-compliant systems while concurrently serving its own capabilities to others. This facilitates more innovative agent design, where every component — be it a data source, model, or business logic module — can be abstracted as a reusable service. This modularity makes it easy to introduce new capabilities, reuse proven models, and develop complex workflows quickly. 

MCP server and client roles within Astera AI don’t exist or operate in isolation. Rather, they work together. For instance, an agent can act as a client by calling an external service to retrieve insights, then switch roles immediately to serve those insights to other agents. When every agent is able to both consume and serve, bidirectional communication becomes the standard, removing the need for rigid hierarchies. 

In multi-agent solutions designed to serve entire departments or organizations, dual MCP support enables AI agents to align around shared objectives while retaining their autonomy. These agents can step in and out of their roles as required, keeping up with workflows as they evolve and scale.

True agent ecosystems need flexible, interoperable architectures. When you work with dual MCP functionality, you ensure that your agentic workflows are as flexible and modular as possible.

Benefits of the Joint Client-Server MCP Model

There are some specific capabilities that are only found in AI agents with dual MCP functionality, such as:

1. Composability and Modularity 

Building layered agent systems is easier with dual-mode MCP. For instance, you can have a high-level ‘orchestrator’ agent (acting as a client) to delegate subtasks to specialized sub-agents (working as servers but also as clients if they require access to tools or services), then utilize these findings to drive its own logic.

2. Simpler Integration 

MCP’s standardized interface frees developers from building custom connectors for each pair of systems. Instead, new tools or data services only need to be exposed as an MCP server once for any agent to act as a client and use them. By that same principle, an agent’s own capabilities also need to be exposed only once as an MCP server to make them ready for use by clients.

3. Runtime Discovery

MCP supports runtime discovery of capabilities, enabling dual-role agents to dynamically find and plug into new servers while also offering new tools to others. This makes it easier to bring in new tools or data sources without altering the agent’s code, with MCP compliance being the only factor.

The Dual MCP Functionality in Action: Data Operations Through a Chat-Based UI 

Astera AI’s dual MCP functionality makes it possible to perform complex data-related tasks — including data preparation, cleaning, extraction, or data warehousing — entirely through a chat-based interface without even using Astera’s drag-and-drop functionality.

Several mechanisms and features come together to bring this use case to life.

1. Conversational Agents with Structured Input and Output 

The platform supports AI agents that can interact with users using structured input and output message formats. These agents can parse incoming user requests submitted through the chat interface (for example: “Clean missing values in customer data”), then convert them into standardized instructions that can trigger predefined logic flows.

Data cleaning using natural language instructions through Astera AI’s chat-based interface

2. Exposing Backend Data Tasks or Triggering External Services 

Dataflows, ETL pipelines, transformations, and logic built on the Astera platform can be published as MCP server endpoints. These endpoints are callable by Astera AI’s chat-based agents. A user can request a task conversationally using simple English-language instructions, and the agent will call the appropriate data operation exactly as if it were dragged and dropped from the platform’s built-in objects. 

A chat-based agent can also act as an MCP client to, for instance, query a database. So even if a particular data task isn’t hosted locally, the agent will still be able to complete it by reaching out to another MCP-compatible service.

3. Built-in Access to Data Functions 

Astera agents can directly access built-in capabilities such as data transformations, profiling, and quality checks, without user intervention. An agent can invoke these native tools in response to chat inputs, since it understands which function to trigger based on the user’s natural language inputs.

4. Prompt Engineering with Real-Time Data Injection 

Astera AI supports structured prompt templates that dynamically inject data from sources such as CRMs or ERPs into agent prompts. This feature enables chat-based agents to intelligently retrieve, transform, or summarize data according to real-time enterprise context. These agents can also conversationally automate data extraction or preparation tasks. 

Together, these features transform the chat UI into a front-end to the entire Astera Data Stack. All capabilities usable through workflows become callable via an agent, as long as they’re appropriately published or exposed, and enterprise data orchestration becomes conversational. 

Dual MCP support is central to this entire use case. As an MCP client, a chat-based agent can initiate actions such as calling an ETL pipeline, cleansing a dataset, or pulling CRM data. As an MCP server, the same agent can share its results and capabilities with other agents or systems, making its work triggerable and usable by others.

Without dual MCP, an agent would only be able to do half the job, decreasing its value in enterprise data automation.

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Summing It Up 

It’s not inaccurate to say that MCP is the “giant leap forward” for ensuring interoperable, standardized communications between AI agents and the systems and services they must work with. 

However, with agentic automation gaining popularity across industries, dual MCP functionality offers more than just an elegant, logical way forward for building more capable and communicative AI agents.  

It’s an exciting, flexible approach that lowers the need for middleware, promotes bidirectional coordination, and turns every agent into a self-contained microservice that can be reused, extended, or fit into larger organizational workflows. This flexibility is necessary for scalable, maintainable, and intelligent enterprise automation. 

Embracing dual MCP functionality means prioritizing composability, interoperability, and agent-based thinking. Giving these factors their due importance can prepare organizations for next-generation enterprise AI success. 

Ready to build dual-MCP agents for your business? Learn more about Astera AI Agent Builder.