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MCP can make it easier for an AI application to connect to tools, data sources, and workflows, but adopting the protocol does not make an agent production-ready. At scale, the hard failures are usually at the boundaries around MCP: permissions that allow more than a tool needs, tool selection that degrades as catalogs grow, retries that repeat side effects, and operational traces that miss model and tool behavior.
A July 28, 2026 revision of the MCP specification changes protocol-level session handling, which affects how servers can be scaled horizontally. It does not remove the need for application state, durable workflow design, authorization, or client/server compatibility checks. AWS’s deployment guidance names five remote hosting patterns; the right choice depends on workload shape, control requirements, security needs, cost model, and the team’s operating skills.
What MCP standardizes—and what it leaves to your team
The MCP documentation describes an open-source standard for connecting AI applications to external systems such as data sources, tools, and workflows. It standardizes an integration interface; it is not, by itself, a production security model, workload governance system, or reliability plan.
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- MCP interface: describes how an AI application interacts with tools and context.
- Authorization: determines what the agent runtime and its tools are actually permitted to do.
- Application reliability: handles retries, timeouts, durable work, side effects, and degraded operation.
- Governance and operations: set limits, assess behavior, and provide evidence of what happened across model and tool steps.
What breaks first as MCP workloads grow
Tool selection gets harder as catalogs expand
A broad catalog consumes context and makes it harder for the model to select the intended tool. Similar or overlapping tools also create ambiguity, while duplicate integrations increase maintenance work. A prototype with a handful of hand-picked tools can hide these problems; a production application with many workflows cannot assume the model will always choose correctly.
AWS recommends workflow-scoped tools, filtering or semantic search where appropriate, and reusable servers when that helps avoid duplicated integrations. Measure tool-selection quality against a regression dataset rather than relying on a few successful demonstrations. Include cases where tools have similar names or capabilities, and track both incorrect selections and failures to select a suitable tool.
Requests exceed safe capacity
Inference, tool execution, and downstream services can each become bottlenecks. A burst that looks modest at the user-request level can translate into many model calls and tool invocations. AWS’s MCP strategy recommends per-user and per-tool rate limits and load shedding; limits should reflect the capacity and risk of the systems behind each tool, not just the MCP server’s request rate.
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- Set rate limits at meaningful boundaries, including individual users and high-impact tools.
- Define what can be rejected, queued, or degraded when dependencies approach capacity.
- Keep the tool catalog scoped so unnecessary choices do not add context and selection overhead.
Multi-step work accumulates latency, cost, and failure points
A single user request may trigger multiple inference calls, tool invocations, memory retrievals, and inter-agent communications. Each step adds latency, cost, and another place where an error or timeout can affect the result. The AWS Well-Architected Agentic AI Lens also identifies stochastic tool use, multi-agent coordination, and persistent-memory integrity, privacy, and cost as design concerns.
Instrument the whole request path, not only the MCP server. A useful trace should connect the user request to model calls, tool choices, tool outcomes, memory operations, and any handoffs. Evaluate behavior as well as service health: a server can return successful responses while the agent chooses the wrong tool or produces an unsuitable outcome.
How the July 2026 MCP revision changes scaling
An AWS Architecture Blog post dated September 1, 2026, describes the MCP specification revision published July 28, 2026. According to that post, the revision removes the initialize handshake and the Mcp-Session-Id header. Each request carries its protocol version and client context, so any compatible server instance can respond. Under the earlier session-based design, horizontal scaling could require sticky routing or externalized shared session state to preserve protocol sessions; the revised stateless protocol no longer requires those mechanisms solely for protocol-session continuity.
This is a protocol-level change, not a declaration that an entire agent application is stateless. Application memory, durable workflow progress, authorization context, long-running work, and external side effects still need explicit handling. Do not remove application state or coordination mechanisms simply because MCP requests no longer depend on protocol sessions.
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Check compatibility before changing the deployment
The specification revision does not make every existing client and server compatible automatically. Before relying on the new request model, check the protocol version and behavior supported by each client and server in the path. Plan a migration that accounts for mixed versions if your rollout cannot update all components at once; the AWS post describes the revision but does not establish readiness for any particular deployment.
Make retries safe for side effects
AWS notes that a client may need to re-issue a call when a response stream breaks. If the first request performed an action but its response was lost, a retry can repeat that action unless the tool contract prevents it. Treat retries as application behavior with consequences, not as a transport detail.
- Identify tools that create, modify, send, or delete external data.
- Design side-effecting operations to be idempotent where possible, so repeating the same operation does not duplicate its effect.
- Make uncertain outcomes visible to callers and operators instead of silently assuming that a timed-out request did nothing.
- Account for the revision’s continuation state and standardized error ranges in client and server handling.
Set the authorization ceiling outside the tool schema
A tool description or prompt is not an AWS authorization boundary. Riggs Goodman III, Principal Solution Architect at AWS, puts the governing principle plainly: “You must assume an agent can do anything within its granted entitlements, whether OAuth scopes, API keys, or AWS Identity and Access Management (IAM) permissions, and design your controls accordingly.” His April 14, 2026 AWS Security Blog guidance recommends narrowly scoped agent roles and organizational guardrails.
Design for the full possible impact of an agent’s granted access, not only the intended use of a particular tool. Map each agent runtime, MCP server, identity source, and direct API path. Then define which permissions are needed, which identity receives them, and how actions will be audited. When the team controls the agent code, AWS describes using AssumeRole with temporary credentials and session policies scoped to a tool invocation.
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- Grant agent roles only the permissions required for their work, and use organizational guardrails to constrain the maximum scope.
- Inspect credentials and direct service paths, not just the tools listed in an MCP catalog.
- Audit actions at the authorization and service boundary as well as at the MCP layer.
- Revisit the access ceiling when adding tools, new workflows, or new ways for an agent to reach a service.
Choose an AWS hosting pattern by workload and ownership
AWS’s deployment guidance names five remote hosting patterns. It advises selecting among them based on scaling, security, cost, and operational requirements; it does not establish a universal winner or comparative performance benchmark. Use the following as a decision checklist rather than as a ranking.
| Pattern named by AWS | Questions to resolve before choosing |
|---|---|
| Amazon Bedrock AgentCore | Which runtime and protocol operations will the team own, and which available managed capabilities fit the workload and its governance requirements? |
| AWS Lambda with Amazon API Gateway | How do request duration, burst patterns, concurrency, and the team’s serverless operating model fit the application? |
| Amazon ECS | How much container-runtime control is required, and can the team operate the selected deployment and networking model? |
| Amazon EKS | Does the workload justify Kubernetes-level control, and does the team have the operational capability to manage it? |
| Amazon EC2 | Does the team need the control of managing instances, and can it own the associated capacity and operational work? |
For any candidate, write down expected concurrency, bursts, latency targets, and workload duration; required identity controls and network connectivity; existing team skills; and how usage and costs will be measured. AWS’s published list supplies the hosting options and decision criteria, not neutral figures for relative price, latency, or scale. Those values need to be established for the specific architecture and workload rather than assumed from the service name.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When a gateway helps—and what it does not solve
AWS describes Bedrock AgentCore Gateway as an entry point between MCP clients and servers that can centralize credentials, observability, and secure connectivity. It can aggregate MCP servers, REST APIs, and Lambda functions. AWS also describes resource-based policies, service control policies, private connectivity options, centralized application and identity logs, and request/response interceptors.
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A gateway can make shared connectivity and governance easier to manage when multiple clients and integrations need a common control point. It does not make an over-permissioned agent safe, remove the need to evaluate tool choices, or prove that a workload meets its latency and availability goals. The AWS product description does not provide a workload-independent cost, latency, or availability comparison, so assess those properties against the intended use and configuration.
A production rollout that exposes failure early
- Inventory the paths. List agent runtimes, MCP clients and servers, identities, tools, memory systems, and any direct API or shell access. This identifies both MCP-mediated and bypass paths.
- Set permission ceilings. Assign narrowly scoped roles and guardrails, then verify that permissions match each tool invocation’s needs. Add temporary credentials and session policies where the implementation can use them.
- Reduce and test the tool surface. Scope tools to workflows, remove duplicate integrations where possible, and build regression cases for ambiguous choices, incorrect choices, and tool failures.
- Test load behavior. Exercise expected concurrency and bursts, set per-user and per-tool rate limits, and define load shedding and graceful degradation before a dependency becomes saturated.
- Make action recovery explicit. Identify side-effecting calls, test interrupted responses and retries, and ensure repeated requests cannot silently cause duplicate actions.
- Trace and evaluate end to end. Correlate model, tool, memory, and inter-agent steps; monitor tool selection and outcomes alongside conventional service telemetry.
- Validate protocol migration. Check the July 28, 2026 protocol revision against the versions supported by all clients and servers before removing session-dependent routing or state that still serves application needs.
- Choose hosting and governance controls together. Select among the five AWS patterns based on workload, security, cost, and operating requirements; decide separately whether a gateway provides useful centralized connectivity and controls.
Apply controls across the AWS Well-Architected categories identified in AWS’s MCP strategy: security, operational excellence, reliability, performance efficiency, and cost optimization. For consequential actions, include an appropriate human-oversight path and a defined degraded mode rather than assuming the agent can always recover autonomously.
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