The architecture distinction is visible before the protocol details begin. A2A operates horizontally between independently operated agents. MCP operates vertically between an AI application or agent and the tools, data, prompts, and enterprise capabilities it consumes.
That difference is the foundation for the entire protocol decision.
Enterprise AI teams are increasingly asking whether they should standardize on Model Context Protocol, Agent2Agent Protocol, or both.
At first glance, this looks like another technology comparison where architects evaluate overlapping features, select a winner, and begin building a platform standard.
That is the wrong way to frame this decision.
MCP and A2A do not compete for the same architecture boundary. MCP standardizes how an AI application reaches external capabilities and context. A2A standardizes how one independently operated agent communicates, collaborates, and delegates work to another agent.
The practical distinction is straightforward:
MCP is for invoking a capability. A2A is for delegating work to an agent.
That does not mean every enterprise tool should be converted into an MCP server. It also does not mean every component described as an agent needs an A2A interface.
The architecture decision depends on who owns the workflow, who controls execution, what state crosses the boundary, and where security and operational responsibility change.
Once those questions are answered, the protocol choice becomes much clearer.
TL;DR
Use MCP when the calling AI application knows which tool, resource, prompt, or operation it needs and retains control of the broader workflow.
Use A2A when one agent delegates an objective to another independently operated agent that controls how the work is completed.
Use both when independent agents collaborate through A2A while each agent accesses its own governed tools and data through MCP.
Use neither when a direct API, internal function call, deterministic workflow, or message contract is simpler and easier to operate.
The most important question is not whether the work is simple or complex. It is whether the remote participant is a capability being invoked or an agent being trusted to own delegated execution.
Why This Comparison Matters in 2026
MCP and A2A have both matured significantly from their early experimental positioning.
As of July 17, 2026, the current stable MCP specification remains version 2025-11-25. A release candidate for the next specification, identified as 2026-07-28, is available ahead of its planned final release on July 28, 2026.
The release candidate introduces substantial changes, including a stateless protocol core, a formal extension framework, redesigned task support, authorization improvements, and deprecation rules. Because it contains breaking changes, production architects should distinguish between the current stable specification and the upcoming revision rather than treating release-candidate behavior as universally available.
A2A reached its production-ready 1.0 milestone in March 2026. The latest patch release is version 1.0.1, published in May 2026.
The increasing maturity of both protocols is useful, but it also creates confusion. Both now include capability descriptions, authentication models, structured messages, asynchronous execution patterns, task state, and mechanisms for work that does not complete immediately.
Those similarities make the protocols appear interchangeable.
They are not.
The durable difference is not synchronous versus asynchronous execution. It is not short-running versus long-running work. It is not simple tools versus complex tasks.
The durable difference is capability access versus delegated execution ownership.
Scope and Terminology
A useful comparison requires clear definitions.
An MCP server is a protocol endpoint that exposes focused capabilities through tools, resources, or prompts. It may run locally or remotely. The MCP host coordinates the AI application, manages client connections, applies consent and security controls, and decides how tool results or retrieved context are incorporated into the application workflow.
An A2A agent is an independently addressable agentic service. It advertises its capabilities through an Agent Card, accepts messages or tasks, and may produce responses, task updates, or artifacts. Its internal reasoning, memory, orchestration framework, models, and tools can remain hidden from the calling agent.
An agent in this article is not merely a function with an LLM call attached. It is a system with some combination of independent planning, task state, authorization, deployment, ownership, lifecycle, and operational responsibility.
This distinction matters because teams frequently call every model-enabled component an agent.
Three prompt-processing functions running inside one Python service do not automatically create an A2A architecture. A remote service that performs a sophisticated workflow does not automatically become an agent either.
Protocol selection should reflect a real system boundary, not a naming convention.
What MCP Actually Standardizes
MCP follows a host-client-server architecture.
The host is the AI application. It coordinates one or more MCP clients and controls how the application interacts with connected MCP servers. Each client generally maintains a dedicated relationship with a specific server.
The server exposes one or more protocol primitives.
| MCP primitive | Primary purpose | Enterprise example |
|---|---|---|
| Tool | Execute a defined operation | Create a ticket, query inventory, run a compliance check |
| Resource | Retrieve contextual information | Read a policy, schema, architecture standard, or configuration record |
| Prompt | Supply a reusable interaction template | Provide a standard change-review or incident-analysis workflow |
The current stable MCP architecture supports local servers using stdio and remote servers using Streamable HTTP.
The most important architectural characteristic is not the transport. It is that the host generally retains responsibility for the broader workflow.
The host or its model decides which capability to use, how calls should be sequenced, what results mean, whether another tool should be invoked, and whether a proposed action requires approval.
An MCP server may contain sophisticated implementation logic. It may call several APIs, validate data, execute automation, or run a long-running backend process.
That sophistication does not automatically make the MCP server an independent agent.
The caller still selects a bounded capability and receives a result that fits into a workflow controlled elsewhere.
MCP Preserves Caller-Led Orchestration
Consider an incident-management assistant.
The assistant might need to:
- Retrieve the affected service from the CMDB
- Query observability data
- Review recent production changes
- Identify the service owner
- Create a draft incident record
- Request approval before initiating remediation
Each of those operations could be exposed through MCP.
The assistant retains the plan. It decides which operation happens next and how the outputs should be combined.
The MCP servers do not need visibility into the entire conversation, every other connected server, or the final user objective. They receive the context and arguments required to perform their exposed operations.
This makes MCP valuable as an integration standard.
Instead of building separate AI-specific connectors between every assistant and every enterprise platform, organizations can publish reusable capabilities through a common protocol contract.
MCP does not replace good API design. It provides a consistent way for AI applications to discover and invoke those capabilities.
What A2A Actually Standardizes
A2A addresses communication between independently operated agentic systems.
The remote participant is not merely a tool endpoint. It is an agent that can receive an objective, interpret the request, manage its own execution, and return results without exposing its internal implementation.
A2A is intended to support agents built using different frameworks, programming languages, vendors, platforms, or organizational ownership models.
Its central concepts include:
| A2A concept | Primary purpose |
|---|---|
| Agent Card | Describes identity, interfaces, capabilities, skills, and authentication requirements |
| Skill | Describes a distinct capability the agent can perform |
| Message | Carries conversational or task-related input and output |
| Task | Tracks stateful work through a defined lifecycle |
| Artifact | Represents a concrete output produced by an agent |
| Part | Carries text, files, structured data, or other content |
A2A version 1.0 supports JSON-RPC, gRPC, and HTTP+JSON protocol bindings. An Agent Card can advertise the interfaces and protocol versions an agent supports.
A2A Preserves Agent Opacity
An A2A client does not need to know whether the remote agent uses:
- One model or several models
- An internal graph-based orchestrator
- A deterministic workflow engine
- MCP servers
- Direct APIs
- Retrieval pipelines
- Human specialists
- A proprietary decision system
The remote agent advertises what it can do, not every internal step it performs.
This is closer to assigning work to another service team than invoking a function from a shared library.
The calling agent defines the objective and the interaction contract. The remote agent owns the implementation method within its boundary.
Consider a change-management agent that needs a formal security assessment.
Calling a tool named query_vulnerability_database is capability invocation.
Sending a request that says, “Assess this proposed production change, identify policy conflicts, calculate the likely security impact, and return an evidence-backed risk artifact” is delegation.
The security agent may query several systems, select its own tools, apply internal policies, request additional context, and produce multiple artifacts.
The calling agent does not need to control or understand every internal step.
That is the boundary A2A is designed to support.
Long-Running Work Is No Longer the Main Differentiator
Earlier comparisons frequently reduced the protocols to a simple rule:
- MCP is for short tool calls
- A2A is for long-running tasks
That distinction is no longer reliable.
A2A includes stateful tasks, task status transitions, polling, streaming, subscriptions, push-notification configuration, cancellation, artifacts, and multi-turn interaction.
MCP’s current stable specification also includes experimental Tasks. MCP Tasks are durable state machines that can support polling, status tracking, cancellation, deferred result retrieval, expensive computations, and batch-oriented operations.
The protocols therefore overlap in their ability to represent work that takes time.
A database export that runs for twenty minutes may still be an MCP tool. The caller selected a defined operation and continues to own the surrounding workflow.
A security classification that completes in two seconds may still be an A2A interaction. The caller delegated an objective to an independent agent that owned the assessment method.
Duration is an execution characteristic.
Ownership is an architecture boundary.
The better question is:
Who owns the method, workflow, and execution state required to complete the work?
MCP vs A2A at a Glance
| Evaluation area | MCP | A2A |
|---|---|---|
| Primary problem | Standardize access to tools, prompts, resources, and context | Standardize communication and delegation between independent agents |
| Remote participant | Focused capability server | Independently operated agentic service |
| Typical request | Execute this operation with these inputs | Achieve or assess this objective |
| Workflow ownership | Calling host usually owns the broader plan | Remote agent owns its internal execution method |
| Internal visibility | Server receives the context required for the operation | Remote agent can remain operationally opaque |
| Discovery model | Server capabilities and exposed primitives | Agent Card and advertised skills |
| Main output | Tool result, resource content, or prompt | Message, task state, status update, or artifact |
| Long-running work | Supported through experimental Tasks in the stable specification | First-class task and collaboration model |
| Common protocol bindings | stdio and Streamable HTTP | JSON-RPC, gRPC, and HTTP+JSON |
| Best-fit boundary | AI application or agent to capability | Agent to independently operated agent |
| Primary security concern | Tool scope, data access, credentials, side effects, and consent | Agent identity, delegation authority, task visibility, and transitive trust |
| Common architecture failure | Treating protocol compatibility as trust | Delegating work without bounding authority or downstream actions |
The comparison should remain centered on responsibilities rather than feature counts.
Feature-based protocol selection often fails because both protocols now include discovery, authentication patterns, structured messages, state, and asynchronous behavior.
The meaning of the remote participant remains different.
The Architecture Decision Framework
The fastest way to choose the correct boundary is to ask whether the remote participant is a capability or an agent.
The Remote Participant Is a Capability When
The caller knows the operation it wants performed.
The request has a bounded input and output contract.
The caller controls sequencing, retries, policy checks, and the surrounding workflow.
The remote system does not need broad freedom to choose among several strategies.
The caller expects a result from an operation rather than collaboration from an autonomous participant.
That points toward MCP, a direct API, or an internal function contract.
The Remote Participant Is an Agent When
The caller delegates an outcome rather than selecting a precise operation.
The remote system can decide how to achieve that outcome.
The remote system may select its own models, tools, data sources, or sub-workflows.
The interaction may require clarification, intermediate updates, negotiation, authorization, or several artifacts.
The remote system has an independent deployment lifecycle, owner, vendor, security boundary, or operational support model.
That points toward A2A.
The Fast Decision Matrix
| Architecture question | Likely direction |
|---|---|
| Does the caller know the exact operation to invoke? | MCP or direct API |
| Does the caller retain the workflow plan and state? | MCP |
| Can the remote participant choose its own execution strategy? | A2A |
| Is the remote participant operated by another team or vendor? | A2A |
| Are several agents only internal nodes inside one runtime? | Neither may be required |
| Do independent agents each require governed tool access? | A2A between agents, MCP beneath them |
| Is the integration simple, stable, and application-specific? | Direct API may be enough |
| Is every step deterministic and known in advance? | Workflow engine may be better |
When MCP Is the Right Choice
MCP is appropriate when an organization needs a reusable, model-aware integration layer.
Common examples include:
- Exposing CMDB lookup capabilities to several AI applications
- Providing controlled read and write access to an ITSM platform
- Publishing architecture standards as contextual resources
- Allowing approved assistants to query observability systems
- Standardizing access to internal APIs across several model providers
- Providing a governed catalog of infrastructure automation tools
- Exposing reusable prompts for incident, change, or compliance workflows
The defining characteristic is that the host still owns the workflow.
An operations agent may decide to retrieve service ownership, inspect telemetry, review recent changes, and create a draft ticket. Each operation can be implemented through MCP while the operations agent controls the sequence.
MCP Should Not Become a Universal Wrapper
Not every API needs an MCP server.
A direct API may remain the better design when:
- Only one application consumes the service
- The contract is already stable and well governed
- Dynamic capability discovery provides little value
- The application is not model-driven
- Additional protocol infrastructure would increase operational burden
- Existing identity and API-management controls already solve the problem
MCP should reduce integration complexity, not simply move it into another layer.
When A2A Is the Right Choice
A2A becomes valuable when collaboration crosses a genuine agent boundary.
That boundary may exist because of:
- Separate organizational ownership
- Separate deployment and release cycles
- Different agent frameworks
- Different cloud or platform environments
- Vendor-operated agent services
- Distinct security or regulatory domains
- Independent task state
- A requirement to hide internal implementation
- A need to return multiple artifacts or intermediate updates
A2A is especially useful when the remote system should remain replaceable without forcing the caller to understand its internal orchestration.
An architecture-review agent might delegate a compliance assessment to a governance agent. The governance agent could inspect policy repositories, evidence stores, cloud configurations, identity relationships, and regulatory controls.
The architecture-review agent needs the result, assumptions, evidence, and risk classification.
It does not need ownership of the governance agent’s internal tool sequence.
When the Architecture Needs Both
Many enterprise architectures will use MCP and A2A together.
Consider an IT operations workflow.
A user asks an Operations Agent to investigate a recurring production outage and recommend a corrective change.
The Operations Agent retrieves service ownership, topology, incident history, and recent changes through MCP.
It determines that the proposed remediation may alter an identity or network boundary, so it delegates a security-impact assessment to an independently operated Security Agent through A2A.
The Security Agent receives the objective and manages its own task. It may use MCP to query a SIEM, retrieve security policies, inspect vulnerability data, and validate identity relationships.
The Security Agent returns an artifact containing its findings, assumptions, evidence, and risk classification.
The Operations Agent combines that artifact with operational telemetry and develops a change proposal.
A policy layer determines whether human approval is required.
After approval, the Operations Agent calls a tightly scoped MCP write tool to create the change record or initiate the approved automation.
The protocols solve different parts of the workflow.
A2A provides the delegation and collaboration contract.
MCP provides the capability-access contract.
The orchestration and governance layers still determine whether the full workflow is permitted, observable, recoverable, and supportable.
When You Should Use Neither
Open protocols provide interoperability, but they are not free.
Every protocol introduces:
- SDK and version dependencies
- Authentication configuration
- Connection management
- Discovery and registry requirements
- Compatibility testing
- Retry and timeout behavior
- Telemetry requirements
- Additional operational ownership
- Upgrade and deprecation planning
A direct function call may be correct when several components live inside one runtime and share the same lifecycle.
A direct API may be correct when one application consumes one stable service through an explicit contract.
A deterministic workflow engine may be better when every execution step is known in advance and model-selected orchestration would add unnecessary variability.
An event bus may be better for high-volume notifications or telemetry distribution that does not require agent task semantics.
The correct architecture is the simplest one that preserves the necessary boundaries.
Protocols should be introduced because interoperability, independence, or governance requirements justify them, not because the protocols are receiving industry attention.
Governance Must Cover Three Trust Boundaries
A combined MCP and A2A system creates at least three distinct trust boundaries.
These boundaries should not inherit trust automatically.
User to Orchestrating Agent
The orchestrating agent needs to know:
- Who initiated the request
- What authority that person has
- Which data domains they may access
- Which actions they may request
- Whether they can delegate work to another agent
- When another approver is required
A broad user objective should not become unlimited execution authority.
Agent to Agent
The A2A layer needs to govern:
- Which remote agents may be discovered
- Which Agent Cards are trusted
- Which advertised skills may be invoked
- Which identities may invoke each skill
- Whether cross-tenant tasks are allowed
- How long task state and artifacts are retained
- How far an agent may delegate work
- Which data may cross the agent boundary
An Agent Card describes an agent. It does not automatically establish trust.
Discovery should feed a policy decision, not replace one.
Agent to Tools and Data
The MCP layer needs to govern:
- Which servers are approved
- Which tools and resources are exposed
- Which operations are read-only
- Which operations cause side effects
- Which scopes are required
- Whether human approval is required
- How arguments are validated
- How downstream credentials are obtained
- How tool results are classified and logged
MCP security guidance explicitly rejects token passthrough, where a server accepts a token that was not issued for that server and forwards it to another API.
The guidance also recommends progressive, least-privilege scopes instead of broad wildcard permissions.
A2A similarly expects production deployments to use encrypted transport, authenticate every incoming request, and apply authorization based on identity, skills, actions, and data access.
Each protocol hop requires its own authorization decision.
A Practical Protocol Boundary Policy
The following YAML is not part of either protocol specification. It represents the type of enterprise control contract that should sit around MCP and A2A.
protocol_boundary_policy:
a2a:
allowed_agents:
security-review:
discovery_source: curated_registry
verify_agent_card: true
allowed_skills:
- assess_change_risk
max_delegation_depth: 1
max_task_age: 30m
allow_cross_tenant_tasks: false
mcp:
allowed_servers:
change-management:
read_tools:
- get_change
- list_related_incidents
write_tools:
- create_change
- update_change
write_requires:
- human_approval
- exact_payload_hash
- approved_change_window
identity:
forward_original_user_token: false
require_per_service_credentials: true
preserve_user_subject_for_audit: true
telemetry:
propagate:
- trace_id
- user_subject
- a2a_task_id
record_agent_selection: true
record_mcp_tool_calls: true
record_policy_decisions: true
The names, skills, tools, and limits should be adapted to the actual environment.
The important design choices are:
- Agent discovery is limited to a curated source.
- Only approved agent skills may be invoked.
- Delegation depth is bounded.
- MCP write operations receive stronger controls than read operations.
- The original user token is not blindly forwarded.
- A shared trace connects the user request, A2A task, policy decision, approval, and MCP tool call.
A successful implementation should produce a complete audit path from the original request to the final business outcome.
A weak implementation usually produces separate logs for the chat session, remote agent, policy engine, and tool call with no reliable way to prove they belong to the same transaction.
Observability Must Follow the Work
MCP and A2A do not remove the need for an enterprise observability model.
For every A2A interaction, record:
| Telemetry | Operational purpose |
|---|---|
| Calling and remote agent identity | Establish ownership and trust path |
| Selected skill | Explain why the remote agent was invoked |
| Task and context identifiers | Track state across exchanges |
| Delegation depth | Detect uncontrolled agent chains |
| State transitions | Identify where work stalled or failed |
| Artifact provenance | Show which agent produced each result |
| Cancellation and retries | Detect duplicate or abandoned execution |
For every MCP interaction, record:
| Telemetry | Operational purpose |
|---|---|
| Server and tool identity | Identify the invoked capability |
| Tool schema or implementation version | Detect contract drift |
| Input classification | Protect sensitive arguments |
| Authorization decision | Prove the operation was permitted |
| Latency and result status | Support reliability analysis |
| Side effects | Record what changed |
| Approval reference | Connect execution to authorization |
At the end-to-end level, preserve:
- User identity
- Trace identifier
- A2A task identifier
- Remote agent and skill
- MCP server and tool
- Policy decision
- Approval record
- Artifact provenance
- Final business outcome
Observability should show not only what happened, but why one agent selected another agent and why a tool invocation was authorized.
Failure Handling Must Be Designed Before Delegation
Distributed agent systems can fail in more ways than conventional tool integrations.
A minimum production test plan should cover:
| Failure condition | Required control |
|---|---|
| Agent Card changes unexpectedly | Reject, revalidate, or require approval |
| Remote agent becomes unavailable | Bounded retry, fallback, or safe termination |
| A2A task exceeds its limit | Cancel and escalate |
| Duplicate update or notification arrives | Process idempotently |
| Authorization expires during execution | Pause and request renewed authorization |
| MCP tool times out | Retry only when the operation is safe and idempotent |
| Tool performs a partial write | Detect, reconcile, or roll back |
| Agent returns incomplete artifacts | Mark the outcome incomplete |
| User cancels the workflow | Propagate cancellation across active work |
| Remote agent delegates again | Enforce delegation depth and destination policy |
A protocol can represent state and errors.
The enterprise platform must still decide how those states affect the business workflow.
Common MCP and A2A Misunderstandings
An MCP Server Is Not Automatically an Agent
An MCP server is a provider of protocol capabilities.
It may contain complex logic, but complexity alone does not create an independent agent boundary.
Treat it as an agent only when the system genuinely accepts delegated objectives, owns task execution, and operates with meaningful independence.
A2A Does Not Replace the Orchestrator
A2A standardizes communication, discovery, task interaction, and artifact exchange between agents.
It does not determine the organization’s global workflow.
It does not resolve every conflict, decide which agent is authoritative, define business accountability, or enforce every enterprise policy.
An orchestrator may use A2A.
A2A is not the entire orchestration layer.
Multiple Agents Do Not Automatically Require A2A
Several specialized components inside one runtime may share the same memory, deployment, identity, framework, and owner.
Adding A2A between them can introduce network and lifecycle complexity without creating a useful architecture boundary.
A2A becomes valuable when the separation needs to survive changes in framework, team, vendor, deployment, or platform.
A2A Is Not Only for Long Tasks
Both protocols can support deferred or long-running execution.
The distinction is ownership, not elapsed time.
Open Protocol Does Not Mean Trusted Protocol Participant
A protocol can standardize communication without approving the sender.
It can describe capabilities without proving those capabilities should be trusted.
It can advertise authentication requirements without designing the organization’s identity architecture.
MCP and A2A still require:
- Identity mapping
- Authorization policy
- Network controls
- Registry governance
- Credential isolation
- Least privilege
- Audit retention
- Approval paths
- Incident response
- Version management
Protocol adoption is an integration decision.
Trust remains an enterprise architecture decision.
A Practical Adoption Sequence
Begin with architecture boundaries rather than SDK installation.
Inventory the Participants
Document every model application, agent, tool, data source, API, workflow engine, and human approval point.
Identify the technical owner, business owner, security boundary, and deployment lifecycle of each participant.
Classify Each Remote Participant
Determine whether each participant is:
- A bounded capability
- An independently operated agent
- An internal implementation detail
- A deterministic service
- A human decision point
This classification should determine the protocol.
The component’s product name or marketing description should not.
Start With the Simplest Valid Contract
Use direct functions or APIs for small, stable integrations.
Introduce MCP when reusable tool and context integration becomes an architecture requirement.
Introduce A2A when agent independence, delegated execution, and interoperability become real requirements.
Build the Control Plane With the Protocol
Do not postpone:
- Agent and server registration
- Identity mapping
- Skill and tool allowlists
- Write-operation controls
- Delegation limits
- Approval policies
- Trace propagation
- Version compatibility
- Emergency revocation
A proof of concept that moves messages proves only that communication works.
It does not prove that the system can operate safely.
Test Failure as Seriously as Success
Test unavailable agents, changed Agent Cards, expired credentials, duplicate updates, canceled tasks, partial artifacts, tool timeouts, policy denial, and non-idempotent writes.
The architecture is ready when failures produce bounded, understandable, and recoverable outcomes.
It is not ready merely because the demonstration path succeeds.
Conclusion
MCP versus A2A is not a winner-takes-all protocol decision.
It is a boundary decision.
Use MCP when an AI application or agent needs a standard way to discover and invoke tools, data, prompts, resources, and enterprise capabilities.
Use A2A when an agent needs to delegate an objective to another independently operated agent without taking ownership of that agent’s internal execution method.
Use both when independent agents collaborate while relying on governed capabilities beneath them.
Use neither when a simpler interface is easier to test, secure, and operate.
The most useful architecture question is not:
Should we standardize on MCP or A2A?
It is:
Is the other side a capability we invoke, or an agent we trust to own delegated work?
That question exposes orchestration authority, security boundaries, operational ownership, task-state responsibility, and failure accountability.
Those decisions determine whether an agentic architecture remains a collection of impressive integrations or becomes a maintainable enterprise platform.
External References
Model Context Protocol Specification, version 2025-11-25
https://modelcontextprotocol.io/specification/2025-11-25
Model Context Protocol Architecture Overview
https://modelcontextprotocol.io/docs/learn/architecture
Model Context Protocol Tasks, version 2025-11-25
https://modelcontextprotocol.io/specification/2025-11-25/basic/utilities/tasks
MCP 2026-07-28 Specification Release Candidate
https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/
Model Context Protocol Security Best Practices
https://modelcontextprotocol.io/docs/tutorials/security/security_best_practices
Agent2Agent Protocol Specification
https://a2a-protocol.org/latest/specification/
A2A Protocol Version 1.0 Announcement
https://a2a-protocol.org/latest/announcing-1.0/
A2A Protocol Release History
https://github.com/a2aproject/A2A/releases
A2A Core Concepts
https://a2a-protocol.org/latest/topics/key-concepts/
A2A Agent Discovery and Agent Cards
https://a2a-protocol.org/latest/topics/agent-discovery/
Enterprise Implementation of A2A
https://a2a-protocol.org/latest/topics/enterprise-ready/
TL;DR An agent action is not complete when the MCP server returns a successful response. It is complete only when the runtime…
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