MCP-A2A Interoperability in Practice: Three Reflections from a PoC Builder

As the agent protocol ecosystem splits into two camps—MCP (Model Context Protocol) and A2A (Agent-to-Agent)—the seemingly benign word "interoperability" often translates, in engineering practice, into a large amount of hidden friction cost. A developer shared on Reddit their experience building a PoC (proof of concept) bridging MCP and A2A, and the conclusion was blunt: interoperability is far harder than it looks. This isn't a post trashing the protocols—it's a clear-eyed retrospective from someone who wrote the code.

Why bridge MCP and A2A

MCP addresses the question of "how agents connect to external tools and data sources," with its core abstraction being capability exposure in a client-server model; A2A focuses on "how agents collaborate to complete tasks," emphasizing peer-to-peer communication and task delegation. The two were designed with different goals from the start, but in real-world scenarios, an agent that connects to tools via MCP frequently needs to hand a task off to another agent that exposes its capabilities via A2A—hence the need for a bridge.

The author's motivation for building the PoC was precisely to connect these two communication paradigms so that tool invocation and agent collaboration could flow naturally within a single process. It sounds like mere translation work between two APIs, but once hands hit the keyboard, it became clear that misalignment of protocol semantics is far more troublesome than differences in interface formats.

MCP (Model Context Protocol) was proposed and open-sourced by Anthropic in late 2024, positioned to let models access external tools, resources, and prompt templates in a standardized way. It uses JSON-RPC communication and builds client-server interaction around three primitives: tools, resources, and prompts. A2A (Agent-to-Agent Protocol) was released by Google in 2025 and donated to the Linux Foundation, aimed at task delegation and collaboration between agents. Its core concepts include Agent Card (a metadata document describing an agent's capabilities and endpoints), Task (a long-trackable task object), and message flows. One reaches outward to tools, the other connects inward to peers—together they cover the two communication directions of an agent system, which is exactly why they're often used together in practice, giving rise to bridging needs.

The real difficulty of interoperability isn't at the protocol translation layer

From an engineering perspective, mapping MCP requests into A2A message formats, or vice versa, is not technically infeasible. The author did get the basic pipeline working in the PoC, but the problems that surfaced along the way were concentrated at the semantic level.

Asymmetry of capability descriptions

MCP uses tool schemas to precisely define what parameters a capability accepts and what structure it returns; A2A's capability expression leans more toward task-level descriptions with coarser granularity. When a bridge tries to wrap an A2A agent as an MCP tool, the absence of parameter constraints directly prevents the caller from reliably constructing requests. The author points out that this asymmetry can't be eliminated by adding a layer of adapter code—it stems from the two protocols' differing definitions of the very concept of "capability."

Agent Card is the core mechanism in A2A for describing agent capabilities. It declares in JSON the agent's name, description, supported skills, input/output modalities, authentication methods, and service endpoint URL, making it easy for other agents to discover and invoke it automatically. But the skills field in an Agent Card is mostly natural-language descriptions plus a few examples, lacking formal constraints on parameter types, required fields, and value ranges like those in JSON Schema. This is precisely the main obstacle when wrapping an A2A agent as an MCP tool during bridging: MCP's tool schema requires field-by-field definitions of types and constraints, and the calling model generates structured parameters accordingly, whereas the A2A side lacks equivalent formal information, leaving the adaptation layer to infer schemas or write them by hand.

Conflicts in lifecycle and state management

MCP calls are typically short-lived request-response patterns, whereas A2A task delegation may span a long period, involving multiple rounds of state updates and asynchronous callbacks. When handling this lifecycle mismatch in the PoC, the bridge layer had to take on additional state-tracking responsibilities, going beyond the role of a "translator" to become a quasi-coordinator. The author admits this complexity was severely underestimated in the initial design.

A2A's task model is designed for long-running scenarios: a task delegation produces a Task object with a unique ID, whose state transitions among submitted, working, input-required, completed, failed, and so on. Progress updates can be streamed via SSE during this period, and push notification callbacks are also supported. MCP's tools/call, by contrast, is a typical synchronous request-response; even with progress notifications, the caller still expects a result within a single session. The bridge layer therefore needs to map A2A's Task state machine into intermediate states comprehensible to the MCP caller, and handle the case where a task keeps running in the background after the session disconnects—which effectively requires the bridge to persist a task mapping table and implement polling or push forwarding.

Error semantics are hard to align

The two protocols define and propagate failure differently. MCP error codes tend to be oriented toward tool-execution exceptions, while A2A must account for task-level failure and retry semantics. When the bridge layer translates errors, information loss is nearly inevitable, and the error description the downstream caller receives may already have drifted from the original cause of failure. This is a real threat to observability in production environments.

A judgment on the protocol ecosystem

This developer's PoC experience points to a broader conclusion: the cost of protocol interoperability often lies not in the protocol specifications themselves, but in the semantic gaps the specs don't cover. Standards bodies can define message formats, but it's very hard for them to define mapping rules between two protocols' conceptual models—that work can only be probed and accumulated by practitioners through code.

For teams planning similar bridging work, the author's experience offers several signals to watch: assess the feasibility of semantic alignment before writing any adapter layer; factor state-management complexity into schedule estimates; and design traceable logging strategies for error translation. Interoperability isn't a one-off engineering task—it's an ongoing process of semantic negotiation that requires continuous maintenance.

The trend toward MCP-A2A convergence remains a hot topic in the community, but this PoC reminds us that "connectivity" and "interoperability" between protocols are two different things. The former relies on interfaces; the latter relies on deep understanding of semantics and design by compromise.

Source: a retrospective on an MCP-A2A bridging PoC shared by a developer on Reddit.

One-line Summary

A developer building an MCP-A2A bridge PoC found that the hard part of interoperability is semantic misalignment, not interface translation.

Paragraph Summary

A developer shared on Reddit a retrospective of building a PoC bridging MCP and A2A, noting that protocol interoperability is far harder than it appears. MCP focuses on agents connecting to external tools and data sources, while A2A focuses on task delegation and collaboration between agents; the bridging need arises from connecting the two in real workflows. Although the PoC got the basic pipeline working, the real problems were concentrated at the semantic level: the granularity asymmetry between MCP's tool schemas and A2A's task-level capability descriptions makes it hard for callers to reliably construct requests; MCP's short-lived request-response pattern conflicts with A2A's asynchronous, long-spanning task model, forcing the bridge layer to take on state coordination; and the two protocols' error semantics are hard to align, with information loss risk during translation. The author concludes that interoperability costs hide in the semantic gaps the specs don't cover, requiring ongoing semantic negotiation and maintenance rather than one-off adaptation engineering.