MCP vs ACP: AI Protocols for Context and Agent Orchestration

🕒 Estimated reading time: 11 minutes

TL;DR: MCP (Model Context Protocol) is the new “universal adapter” that gives AI agents secure, two‑way access to external data sources—databases, file systems, APIs—via a single open standard. ACP (Agent Communication Protocol) is the “agent bus” that lets multiple AI agents talk, delegate, and orchestrate tasks in a vendor‑agnostic way. Used together, they power modular, context‑aware multi‑agent systems where each agent can fetch live data (via MCP) and collaborate seamlessly (via ACP).

Lately I have been starting playing with MCP and I started creating small use cases with it in my homelab, so I gathered some information about it and I wanted to share it with you. I hope you find it useful. 🎉

Modern AI systems are becoming more context-aware and interconnected. As developers(or vibe coders😛) integrate Large Language Model (LLM) agents into real-world workflows, two emerging standards have gained attention in the AI protocol architecture space: MCP (Model Context Protocol)From Anthropic and ACP (Agent Communication Protocol)From IBM. Both aim to make AI agents more capable – MCP by giving AI access to external data and tools, and ACP by enabling rich agent-to-agent orchestration and communication. In this article, we’ll explain each protocol, compare how they operate, and show how MCP and ACP can work together for context-aware AI communication and multi-agent coordination.

What is MCP (Model Context Protocol)?

Model Context Protocol (MCP) is an open standard introduced by Anthropic in late 2024 to bridge AI assistants with the external data sources and tools that contain the context we need in day-to-day tasks. Think of MCP as a universal adapter (the “USB-C for AI applications”) that lets an AI model plug into any structured data source or service. Before MCP, connecting an AI agent to, say, your GitHub issues, a SQL database, or Google Drive required a custom integration for each system. MCP replaces these one-off connectors with a single standardized protocol.

.How MCP Works:.> MCP follows a straightforward client-server architecture. An AI application (client) connects to an MCP server that exposes a particular data source or service. The MCP server is a lightweight adapter that knows how to talk to a specific system (for example, a PostgreSQL database or a GitHub-Gitlab repository) and presents a standardized interface to the AI client. Multiple MCP servers can run in parallel, each providing access to a different tool or dataset. On the client side, an AI agent or platform can query these servers uniformly. This design decouples AI logic from data-access logic, offering flexibility – you can swap out or add new data sources without changing the agent’s core code.

Key Features of MCP:

Secure, Two-Way Data Access: MCP allows AI agents to not only read from data sources but also write or take actions, with proper security controls. For example, an AI agent could retrieve documents from Google Drive or create a new issue in Jira via an MCP integration, all through the same protocol.

Pre-Built Integrations: A growing ecosystem of MCP servers is available out-of-the-box. Anthropic and the open-source community have created adapters for popular platforms like Google Drive, Slack, GitHub, Git, and Postgres, as well as tools like Puppeteer for web browsing. This means an AI agent can gain instant capabilities (e.g. codebase knowledge, file system access, etc.) by connecting to existing MCP servers.

Vendor and Model Agnostic: MCP is model-agnostic – any LLM or AI system can use it. It provides consistent APIs to fetch context or execute tool actions, no matter which AI model or vendor is behind the scenes. This abstraction even allows switching out the LLM itself without losing integration with data sources.

Secure and Infrastructure-Friendly: MCP is designed with security in mind, keeping data within your infrastructure when needed. For instance, you might run MCP servers on your own cloud or on-premises environment so that sensitive databases aren’t directly exposed to the public internet. The protocol encourages best practices like authentication and sandboxing to protect data. (We’re likely to see significant gaps in this area, and in my opinion, substantial development is still needed before it’s safe for enterprise production use.)

MCP as a Universal Data Bridge: The Model Context Protocol provides a universal, open connector linking AI systems to external data sources, replacing fragmented one-off integrations with a single standardized interface. In the abstract illustration below, the diverse shapes on the left represent different data repositories (files, databases, APIs), and the circle on the right represents an AI assistant; MCP is the connecting bar (like a cable) bridging them. By standardizing this link, MCP enables AI models to retrieve and update information across formerly siloed tools and datasets in a secure, consistent manner.

MCP in practice: Imagine you have a coding assistant AI that needs up-to-date context from your company’s GitHub and an internal database. Using MCP, you could run an MCP-GitHub server and an MCP-Postgres server. Your AI agent (the MCP client) can ask these servers for data, such as “Get all open issues labeled ‘bug’ in repo X” or “Fetch user records for customer Y”. The responses come back in a structured format the AI understands. This setup significantly simplifies context-aware AI communication – the agent can seamlessly incorporate external knowledge into its reasoning or outputs. Developers don’t need to handcraft API calls in prompts; instead, the AI uses the MCP interface to interact with live data.

What is ACP (Agent Communication Protocol)?

While MCP focuses on connecting AI to data, Agent Communication Protocol (ACP) focuses on connecting AI agents to each other (and to orchestrators) in a standardized way. ACP is a protocol spearheaded by IBM’s BeeAI team to enable robust agent-to-agent collaboration and orchestration. In complex systems, you may have multiple specialized agents (for example, one agent might be a planner, another a coder, another a tester, another for QA etc.). ACP provides a common language and framework for these agents to communicate, share tasks, and coordinate actions without being tightly coupled to a single vendor or framework. (Think it like a Orchestrator for Agents, like Openstack for VMs and K8s for Pods)

How ACP Works: In traditional distributed systems, you often see a central message bus or broker mediating communication. For example, IBM’s own DevOps build agents communicate with a central server over a messaging protocol (JMS) to receive tasks and report results. Similarly, ACP defines a message-based communication layer for AI agents. Each agent runs as a lightweight process (which could be a container or microservice) that registers with an ACP-compatible agent server or hub (like the BeeAI server). Agents send and receive messages, which can include natural language instructions, structured data, or references to capabilities. The ACP hub routes these messages to the appropriate agent(s), enabling a form of PUB-SUB or Request-Response patterns among agents.

Crucially, ACP is designed specifically for the nuances of LLM-based agents. This means it accounts for things like:

Natural Language Interactions: Agents often communicate with instructions or information in natural language. ACP supports this flexibility, allowing "fuzzy" or high-level requests to be exchanged, not just rigid API calls.

Capability Invocation: Each agent may expose certain capabilities or tools (think of them as functions the agent can perform, like querying a database or summarizing a document). ACP messages can target a specific capability of an agent, enabling one agent to ask another to perform a task on its behalf. This capability-based invocation means an orchestrator agent could say, “Agent B, please execute your query_database capability with XYZ parameters,” in a standardized format.

Orchestration and Role Hierarchy:(one of my favourite topics EMOJI) In multi-agent systems it’s useful to assign roles (e.g. an Orchestrator agent supervises Specialist agents). ACP doesn’t enforce a particular hierarchy, but it makes it easier to build one. Because communication is standardized, an orchestrator agent can delegate tasks to various worker agents and aggregate their responses. (In community discussions, proposals exist for formal agent role taxonomies with associated capabilities, which could be implemented on top of ACP.)

Why ACP Matters: Current multi-agent setups often suffer from each agent framework having its own interface or API, making it hard to get different agents to work in unison. ACP tackles this by providing a universal communication bus for agents. As IBM’s Kate Blair put it, “ACP will act like a universal connector, providing a standardized way for agents to exchange information and interact with other systems.”

By standardizing agent interaction, ACP brings several benefits:

Interoperability: Agents built in different languages or frameworks can talk to each other if they speak ACP, much like how any device speaking HTTP can interact on the web. This agent orchestration across platforms reduces siloed AI behaviors.

Simplified Development: Developers can mix and match pre-built agents (planners, analysts, etc.) without writing glue code for each pairwise integration. They can focus on the higher-level logic (who should do what), while ACP handles message routing and format.

Small tip, mermaid graphs rock, use them in your README.md in your repos too. 😛

Reusability and Modularity: Capabilities developed for one agent (say a PDF parsing tool agent) can be reused by other agents through ACP calls, rather than reimplementing that functionality everywhere.

Progressive Standardization: The BeeAI team is evolving ACP in a feature-driven way – experimenting with what agent communication patterns are most useful, then standardizing them. This ensures the protocol stays practical and avoids over-engineering. Although in “alpha” phase, ACP’s goal is to become a stable, open standard (IBM has open-sourced the draft specification and invited community input).

In essence, ACP serves as an agent orchestration backbone, letting multiple AI agents form a collaborative ensemble. Whether two agents are exchanging information to refine a plan, or a dozen agents are coordinating on a complex workflow (with some handling user interaction, others crunching data), ACP provides the lingua franca for that coordination.

MCP vs ACP: Different Goals, Complementary Roles

It’s clear that MCP and ACP address different layers of the AI stack:

MCP is about agent-to-data/source communication. It connects AI agents to external systems – databases, file drives, APIs – to supply the agent with contextual knowledge or to take actions in those systems. MCP focuses on structured context injection and tool use by an agent.

ACP is about agent-to-agent (A2A) communication. It enables agents to talk among themselves or with an orchestrator, to coordinate tasks and share results. ACP focuses on the interaction and cooperation between multiple agents or agent services.

In practice, these protocols operate on different “buses” in an AI system’s architecture. You can imagine a dual-bus architecture for advanced AI platforms:

1. An internal agent communication bus (ACP) where agents exchange messages, requests, and responses with each other.

2. An external data access bus (MCP) where agents fetch and use context or execute operations on external tools and data sources.

Comparative Example: Imagine an AI system for incident response that involves multiple agents:

• An Orchestrator agent breaks down the high-level task.
• A Data Retrieval agent is responsible for fetching relevant data (from logs, databases, etc.).
• An Analysis agent interprets the data.
• A Report agent writes up the findings.

(Various of such cases can be made)

Using ACP, the Orchestrator can assign subtasks to the Data and Analysis agents and coordinate their outputs, all through well-defined message exchanges (e.g., JSON messages or function calls via an ACP SDK). Now, when the Data Retrieval agent needs to get information from a database, it uses MCP – calling an MCP server that interfaces with the company database. The data comes back to the Data agent (via MCP), and then the Data agent sends the results to the Analysis agent over ACP. Here, ACP handled the workflow orchestration and inter-agent communication, while MCP handled the actual data access. The Analysis agent might similarly use MCP to call a web search tool, etc., then return a conclusion which the Orchestrator uses to compile the final report. This separation of concerns makes the whole system more modular and powerful: new agents can be added (e.g. a Social Media agent to pull in Twitter data via MCP) without disrupting the communication layer, and new tools can be connected (via MCP servers) without needing to rewrite the agent collaboration logic.

Using MCP and ACP Together in Practice

The real strength of these protocols emerges when you use them in tandem. In fact, IBM’s BeeAI platform is doing exactly this: BeeAI’s multi-agent framework leverages MCP for tool integrations and ACP for agent interactions. The BeeAI team built their initial ACP implementation on top of Anthropic’s MCP standard, meaning agents could immediately use MCP-integrated tools as part of their skillset. For example, BeeAI defines “tools” in an agent’s manifest that follow the MCP specification. An agent can invoke a tool (say, a calculator or database query) and behind the scenes that tool call is handled via MCP. The result is returned and then propagated to other agents or the user via ACP messaging.

To illustrate, consider a combined architecture with both protocols:

• All agents run on a platform that provides an ACP message router (allowing any agent to send a message or request to any other agent by name or role).

• Some agents are special-purpose tool adapter agents which essentially wrap an MCP client. For instance, a “DB Agent” might expose a capability query_db. Internally it knows how to communicate with an MCP Postgres server. When any agent sends a message to the DB Agent (via ACP) requesting bash query_db("SELECT ..."), the DB Agent executes that via MCP and then sends back the results over ACP.

• Other agents might directly use an MCP client library to call tools without going through a separate agent – e.g., an agent could directly call an MCP GitHub server to fetch issues. This is also viable if the ACP framework allows agents to use MCP natively. In BeeAI, tools can be accessed programmatically by agents through the ACP SDK or directly via MCP adapters. In fact, integrations exist to use MCP in popular frameworks like LangChain, so an LLM agent can call MCP tools as if they were just another chain component.

Here’s a brief Python pseudo-code example demonstrating an agent orchestrator using ACP and MCP together:

# Example: orchestrator agent sending a request to a data agent via ACP,
# and the data agent using MCP to get the answer.

# Orchestrator agent wants to get user info from a database via the DataAgent.
message = {
    "to": "DataAgent",
    "action": "query_database",
    "params": {"query": "SELECT * FROM users WHERE id=42"}
}
acp.send(message)  # send message over ACP bus

# Inside DataAgent (upon receiving the message):
if message["action"] == "query_database":
    sql = message["params"]["query"]
    # Use MCP client to query the Postgres MCP server
    result = mcp_client.call("postgres.query", {"sql": sql})
    # Send the result back to the orchestrator via ACP
    acp.send({"to": message["from"], "result": result})

In this snippet, the Orchestrator doesn’t need to know how the DataAgent gets the data – only that DataAgent has the capability to handle query_database requests. The DataAgent abstracts the MCP usage. This is a simple illustration of capability-based invocation: one agent invoking another’s capability in a controlled way.

Diagrams & System View: In a visual diagram, one could draw two layers of communication:

• The Agent Communication layer (ACP): depicted as a bus or network connecting multiple agents (nodes). Agents send each other messages over this layer. Think of it as an internal agent dialogue bus.

• The Context/Tool layer (MCP): depicted as various external services (databases, APIs, file stores) each connected to the agents through MCP servers/adapters (like plugins). This forms an external data bus that agents can tap into for information or actions.

The agents sit at the intersection of these two layers – horizontally connected to each other via ACP, and vertically connected to data sources via MCP. Such an architecture can also be deployed in cloud-native environments. For example, each agent (with its MCP tool adapters) could run in a Kubernetes pod, and a central ACP message broker service routes communications. This design lends itself to scalability and resilience. Just as adding more workers in a traditional system increases throughput, adding more specialized agents can increase the system’s ability to tackle complex tasks in parallel.

Conclusion

MCP and ACP represent complementary advances in AI systems design: MCP enriches agents with contextual awareness by bridging them to the outside world of data and services, while ACP empowers the creation of multi-agent ecosystems where agents can cooperate and divide-and-conquer tasks. For AI engineers and DevOps teams, these protocols offer a pathway to build more scalable, modular, and powerful AI solutions – from an intelligent assistant that can pull in any information you need, to a swarm of collaborative agents each handling part of a workflow.

As open standards, both MCP and ACP are being shaped by community involvement and real-world testing. MCP has already gained traction by standardizing tool use for LLMs, and ACP is rapidly evolving with input from projects like IBM’s BeeAI (which invites developers to help define the standard). The convergence of these protocols hints at an AI future where interoperable agents seamlessly weave together knowledge, actions, and coordination. By combining context-aware AI communication with agent orchestration, we move closer to AI systems that are not only smart in isolation, but greater than the sum of their parts.

Anthropic, Introducing the Model Context Protocol https://www.anthropic.com/news/model-context-protocol#:~:text=Today%2C%20we%27re%20open,produce%20better%2C%20more%20relevant%20responses https://www.anthropic.com/news/model-context-protocol#:~:text=MCP%20addresses%20this%20challenge,to%20the%20data%20they%20need https://www.anthropic.com/news/model-context-protocol#:~:text=Claude%203,GitHub%2C%20Git%2C%20Postgres%2C%20and%20Puppeteer

Model Context Protocol Official Docs (modelcontextprotocol.io) https://modelcontextprotocol.io/faqs#:~:text=What%20is%20MCP%3F https://modelcontextprotocol.io/introduction#:~:text=,MCP%20servers%20can%20connect%20to

IBM BeeAI Documentation – ACP Introduction (Alpha spec) https://docs.beeai.dev/acp/alpha/introduction#:~:text=What%20is%20ACP%3F https://docs.beeai.dev/acp/alpha/introduction#:~:text=Current%20agent%20systems%20often%20use,depend%20on%20externally%20hosted%20models https://docs.beeai.dev/acp/alpha/introduction#:~:text=,through%20reuse%20of%20proven%20solutions

IBM Research Blog – BeeAI multi-agent announcement https://nexaquanta.ai/ai-breakthroughs-ibm-meta-and-google-lead-the-charge/#:~:text=One%20of%20the%20most%20significant,and%20work%20together%20across%20platforms https://nexaquanta.ai/ai-breakthroughs-ibm-meta-and-google-lead-the-charge/#:~:text=Built%20on%20Open%20Standards

IBM DevOps Agents Architecture (analogy for agent-server model) https://nexaquanta.ai/ai-breakthroughs-ibm-meta-and-google-lead-the-charge/#:~:text=Built%20on%20Open%20Standards

BeeAI Documentation – Tools and Integration https://docs.beeai.dev/concepts/tools#:~:text=Exposing%20tools%20through%20BeeAI https://docs.beeai.dev/concepts/tools#:~:text=Agent%20usage

• https://github.com/orgs/i-am-bee/discussions/284
• https://github.com/orgs/i-am-bee/discussions/488