Introduction - A Paradigm Shift
AI is undergoing a paradigm shift in communication. Large language models historically have been powerful, but lack access to real-time information or enterprise data without brittle, ad-hoc integrations.
That’s changing with the latest innovations in the agent stack.
Developers are now building for AX (agent experience), and the communication between information and models is at the forefront of active research work.
Enter the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication, two foundational protocols that accelerate how models interact with data and with each other.
Just as HTTP enabled the World Wide Web by providing a standardized way for computers to communicate, MCP and A2A are poised to allow machine-native coordination across tools, data, and agents.
Anthropic’s MCP is an open standard for connecting AI assistants to the data and tools they need, solving the “last mile” of context and action for models. It’s a universal interface that replaces fragmented, one-off connectors with a single plug.
Google’s A2A is an open protocol for multi-agent communication, enabling AI agents, even from different vendors or frameworks, to talk to each other, coordinate tasks, and work together.
Together, MCP and A2A signal a move toward a more interoperable AI stack. Rather than siloed AI services each operating in isolation, we can now build agentic systems that dynamically plug into data sources and collaborate with other agents in a vendor-agnostic way.
Chakra’s role in this ecosystem is to supercharge these rails with a structured data layer. By turning the web and other data assets into a permissionless data store, Chakra ensures AI agents have the real-time information they need to truly operate autonomously.
Model Context Protocol (MCP): Standardizing Access to Data
MCP is the industry’s answer to a long-standing headache: how do we give AI models access to the right context?
Historically, hooking an AI assistant into various data sources (files, databases, APIs, etc.) was tedious and brittle. Developers had to write custom code or plugins for each integration, leading to a combinatorial “M×N” mess of connectors for M models and N tools.
As a result, even powerful LLMs remained trapped behind legacy systems, unable to leverage up-to-date or proprietary data. Anthropic’s Model Context Protocol (MCP) addresses this problem.
Announced in November 2024, MCP provides a universal interface for AI models to interact with external data sources and tools. In other words, it’s a common language that lets any AI assistant talk to any database, knowledge base, SaaS app, or filesystem that implements the protocol.
As we noted in our previous blog post and MCP integration announcement, Anthropic labels MCP a "USB-C port for AI applications.”
Much like how USB or HTTP unified earlier ecosystems, MCP is a missing puzzle piece that can replace fragmented integrations with a single protocol. By defining a standard bi-directional communication channel between AI clients and data/service providers, MCP enables a plug-and-play approach: one standard adapter lets a model access many tools, instead of bespoke adapters for each.
Just as a USB-C port lets one device connect to countless peripherals, MCP acts as a universal adapter for AI, letting models seamlessly interface with:
- Databases and knowledge repositories (from SQL databases to document stores)
- APIs and web services (internal business systems, SaaS APIs, web search results)
- Productivity and dev tools (spreadsheets, calendars, version control, etc.)
- Even custom hardware or blockchain networks (any resource that can implement an MCP server)
Under the hood, MCP operates via a simple client-server model. An AI agent (the MCP client) sends standardized requests to MCP servers, each providing specific data or actions, such as querying a database or accessing calendar events. Servers respond securely, giving agents only authorized access and injecting results directly into their workflows.
This standardized approach offers major benefits:
- Universal integration: Developers write one MCP integration per tool, eliminating custom plugins and drastically simplifying agent-tool connections.
- Real-time context: Agents fetch live data seamlessly (like real-time prices or customer queries), enabling dynamic, relevant responses.
- Actionability: MCP servers allow agents to perform tasks externally (like creating support tickets or executing trades), enabling genuine autonomy.
- Cross-platform consistency: MCP is model-agnostic, supporting popular LLMs like Anthropic's Claude and OpenAI’s ChatGPT, fostering a unified AI interface.
Model Context Protocol (MCP) addresses the complexities of connecting large language models (LLMs) to various external systems, enhancing their functionality and performance.
On May 1st, Cloudflare hosted its first-ever MCP Demo Day, spotlighting how ten leading tech companies—Anthropic, Asana, Atlassian, Block, Intercom, Linear, PayPal, Sentry, Stripe, and Webflow—launched remote MCP servers on Cloudflare’s hosted infrastructure.
This shift from local to cloud-hosted MCP servers in these companies is significant. Now, AI assistants like Claude can manage projects, generate invoices, query databases, and even deploy full-stack applications—all through conversational interfaces accessed via simple URLs. This was a major upgrade - historically, the most challenging part of MCP was local installation.
No local installs. No friction.
By lowering technical barriers and accelerating feature delivery, Cloudflare’s initiative is paving the way for AI-first workflows across industries, where agents can plug into business-critical tools instantly and securely.
Chakra’s mission is to organize the world’s structured data for both humans and agents. We’ve embraced MCP as a natural extension of that goal. By implementing MCP in the Chakra platform, we’ve enabled AI agents to query rich datasets directly via chat with no SQL or API gymnastics needed.
MCP brings data to AI, rather than forcing AI into rigid app workflows. With Chakra as a persistent data layer accessible via MCP, agents can retrieve deeper context grounded on real-world data, improving the quality of output.
Agent-to-Agent (A2A) Protocol: Enabling Collaboration
If MCP standardizes how an AI agent talks to tools and data sources, A2A (Agent-to-Agent protocol) standardizes how AI agents talk to each other.
Announced by Google in April 2025, A2A tackles a complementary challenge: as organizations deploy more autonomous agents to handle various tasks, how can those agents coordinate and collaborate effectively?
Just as humans often work in teams, complex problems (or large workflows) in AI can be better handled by a swarm of specialized agents working together, rather than a single monolithic agent. However, without a common communication protocol, multi-agent systems have been siloed. An agent built by Company A couldn’t easily interact with one from Company B. Google’s A2A changes that, providing an open, vendor-neutral protocol for agent interoperability.
At its core, A2A enables AI agents to discover, communicate, and delegate tasks to one another using a shared protocol. Google describes it as a foundation for a “multi-agent ecosystem” where agents from different platforms can collaborate seamlessly on complex workflows.
By providing a common language for agent coordination, A2A unlocks powerful new capabilities for AI-driven automation and decision-making.
A2A works through four key features:
- Standardized messaging: Built on HTTP and JSON-RPC 2.0 with SSE for streaming, A2A lowers integration friction by using familiar web protocols.
- Agent Cards: Each agent can publish a JSON-based profile describing its capabilities and endpoints, making dynamic discovery and collaboration possible.
- Task-based communication: Agents exchange structured “Tasks” (requests or commands), enabling clear request-response cycles with support for streaming updates.
- Support for long-running workflows: Designed for asynchronous, multi-turn interactions, A2A handles complex processes like research projects or transaction approvals over hours or days (without assuming agents share memory).
Together, these features make A2A the backbone of coordinated agentic workflows, especially in enterprise and decentralized environments.
Unlike monolithic systems, A2A enables dynamic collaboration between specialized swarm agents. As Theoriq CEO Ron Bodkin covered in his recent piece, “A2A represents an important step toward standardized swarm agent interoperability in enterprise environments.”
While MCP and A2A share a common goal of interoperability, they solve different layers of the stack. MCP standardizes how agents access tools and data; A2A standardizes how they interact with one another.
Used together, they enable agent ecosystems that are both modular and collaborative. Agents know how to find information and how to delegate what they can’t do themselves.
A2A has seen strong industry momentum from day one.
Google launched the protocol with backing from Salesforce, SAP, and top AI startups. Microsoft soon followed, signaling A2A’s potential as a cross-cloud standard for agent interoperability.
That momentum reflects a shared belief: universal protocols are critical for collaborative AI to scale, just as shared internet standards unified the early web.
Conceptually, A2A treats agents like microservices in a distributed system, each with a clear interface and lightweight communication layer. While open questions remain around trust, verification, and coordination, A2A lays the foundation for agents to discover and collaborate across networks dynamically.
MCP + A2A: Complementary Interoperability Approaches
It’s important to emphasize that MCP and A2A are not competing standards – they are complementary, each addressing different layers of the AI agent stack.
MCP provides vertical integration (application-to-model), while A2A provides horizontal integration (agent-to-agent). In other words, MCP connects an AI agent downwards into the tools, data, and context of its environment. A2A connects AI agents side-by-side with one another, allowing multiple agents or services to form a network. You can use both at the same time: an agent might invoke MCP to retrieve data and invoke A2A to consult another agent, all within the same project.
Google’s own A2A announcement explicitly notes this synergy: “A2A is an open protocol that complements Anthropic’s Model Context Protocol (MCP), which provides helpful tools and context to agents.”
In practice, this means an agent might use MCP to query a CRM or database, then use A2A to pass that context to a specialized peer, like an analytics agent trained for forecasting. That second agent might use MCP again to retrieve price data, completing its part of the workflow before handing results back. It’s a chain of MCP-powered agents, stitched together via A2A.
A useful analogy: MCP is an agent’s ability to use tools, while A2A is its ability to work in a team.
Just as humans use apps to perform tasks and collaborate with colleagues to divide work, agents equipped with MCP + A2A can operate independently or as part of a larger, modular system.
Looking forward, it's likely that MCP and A2A will evolve to work together. As these protocols mature and converge, they have the potential to create a richer, more interoperable ecosystem of AI agents.
MCP lets any agent tap into our structured data layer, from on-chain analytics to enterprise-grade datasets. With A2A, those agents don’t operate in silos, they can coordinate, delegate, and share workloads across broader workflows.
The Path Forward
MCP and A2A aren’t just protocols, they’re a shift in how we build intelligent systems. By standardizing agent access to tools and each other, these protocols move us from siloed AI to a world of interoperable, autonomous agents.
And when it comes to data, let Chakra handle the heavy lifting. We offer a growing library of structured, real-time datasets (from financials to on-chain analytics) all accessible via open APIs that leverage MCP.
Your agents focus on logic; we’ll power their data.
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