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Build an enterprise AI agent with MCP by treating the protocol as a connection layer—not as the agent’s security or governance system. The host application coordinates the model and creates a client for each MCP server; each server exposes specific data or actions. Your organization must still decide what the agent may do, how user identity and downstream permissions are enforced, and how the system is operated.
What MCP standardizes—and what it leaves to you
The Model Context Protocol (MCP) is an open standard for connecting AI applications to external systems, including data sources, tools, and workflows. Its scope is the exchange of context and capabilities. It does not prescribe the model, the agent’s orchestration, or your organization’s governance design. See the MCP introduction.
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An MCP architecture has three roles: a host, a client for each server connection, and one or more servers. The host is the AI application coordinating the work. A client connects the host to a server, which provides context or tools and may run locally or remotely. The architecture overview describes two protocol layers: a JSON-RPC-based data layer for discovery, capabilities, and interactions, and a transport layer for communication and transport-specific authorization.
Choose the right server primitive
| Primitive | What it provides | Enterprise design implication |
|---|---|---|
| Tools | Executable functions the host can call, such as retrieving a record or initiating an operation. | Treat every tool as a capability. Enforce identity, authorization, input validation, and business policy on the server and, where applicable, in the downstream service. |
| Resources | Context data made available to the host. | Apply the same access controls and data-handling rules used for the underlying information; returned context is not automatically safe to disclose. |
| Prompts | Reusable interaction templates provided by a server. | Use them to guide interaction, not as a substitute for access controls or policy enforcement. |
The client can discover advertised capabilities and call tools according to their schemas, but a schema or a read-only or destructive annotation is not an authorization decision. The server must enforce what the authenticated caller is allowed to do. OpenAI’s MCP server guidance likewise calls for per-request authorization and validation.
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How to shape the enterprise architecture
A useful design separates the agent’s decision-making from the systems that enforce access. The host decides when a capability may be useful; the MCP server authenticates and authorizes the request, validates it against schema and business rules, and calls the appropriate downstream service using its own permitted credentials. The downstream system should continue to enforce its own controls. This way, a model-generated request is never the sole basis for granting access.
- Inventory capabilities. List the data each server can return and each action its tools can perform. Classify actions as read, write, or destructive, and record the downstream systems and permission scopes involved.
- Map identity to permissions. Decide whether each request represents an end user, a service identity, or both. Define how that identity maps to permissions on every downstream operation, then enforce the mapping server-side for each request.
- Keep tools narrow. Prefer a specific operation with a constrained schema over a broad tool that accepts arbitrary queries, destinations, or commands. Separate read operations from writes where practical.
- Make consequential actions deliberate. Require explicit user approval before high-impact writes, and make clear what the tool will change. Approval complements authorization; it does not replace it.
- Define operational ownership. Assign responsibility for server deployment, patching, credentials, availability, monitoring, incident response, and compatibility testing.
MCP’s architecture documentation describes the protocol as stateless: “all the information needed to process a request is contained in the request itself.” A connection or live process is therefore not proof of user identity or a durable conversation boundary. If an application uses a handle to refer to a workflow or object across requests, bind it to the authenticated principal and authorize each request that uses it.
Should you use local stdio or remote Streamable HTTP?
Choose based on the trust boundary and operating model, not on a blanket assumption that one transport is safer or more capable. Local stdio starts a server process alongside the host and communicates through standard input and output. Remote Streamable HTTP connects over HTTP and supports streaming; it can suit a centrally managed service or one serving multiple clients. Compare the actual constraints before committing:
| Decision factor | Local stdio | Remote Streamable HTTP |
|---|---|---|
| Where it runs | Alongside the host, often on a developer workstation or managed local process. | On managed infrastructure reachable over HTTP. |
| Trust boundary | Local code may inherit machine-level access. Startup commands, binaries, and configuration need trusted provenance, careful permissions, and sandboxing. | Network reachability and server-side identity, authorization, and isolation must be designed and operated. |
| Operations | Each host environment may need controlled installation, patching, and configuration. | The organization must own availability, secrets, logging, rate limits, data residency, and network controls for the service. |
| Fit to investigate | Useful when a local process and local access are intentional and tightly controlled. | Useful when centralized operation, shared access, or streaming is required; assess latency, runtime, and network constraints. |
For remote hosting, the deployment can use serverless, containers, edge infrastructure, or a traditional application environment. Choose according to runtime and streaming needs, latency, network access, residency, secret management, observability, rollback, and versioning. The Agents SDK MCP documentation and the deployment guidance above describe implementation considerations, but your identity provider, downstream APIs, regulatory duties, and threat model determine the actual design.
Do not assume older HTTP+SSE examples remain the right choice for a new implementation. The MCP project’s 2026-07-28 specification announcement marks legacy HTTP+SSE as deprecated; check the versions and transport support of the specific clients and servers you plan to use.
How to secure an MCP server
Make the server an enforcement point for every request. The model can propose an action, but it should not decide whether the caller is allowed to access a record or perform that action. Validate inputs against both the advertised schema and business rules, and enforce policy again at the downstream service boundary when available.
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For HTTP authorization, validate the whole OAuth flow
Follow the current MCP authorization specification rather than treating an access token as a general-purpose credential. Its requirements and security considerations are documented in the MCP authorization security considerations.
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- Use resource-metadata-based discovery. The client must include the resource parameter, and the server must verify that an incoming access token was issued for that server.
- Use PKCE and verify PKCE support. Use HTTPS for authorization endpoints and secure redirect URIs.
- Validate redirect URIs exactly. Protect state against cross-site request forgery and replay, and apply the specification’s PKCE-related safeguards.
- Keep tokens separated by resource. The MCP server must use its own separately issued credential for an upstream API; never pass the MCP client’s token through to that API or accept a token intended for a different resource.
Protect consent, credentials, and state
The MCP security best practices explain risks such as confused-deputy behavior in proxy servers. Where consent applies, make it specific to the client and show the requesting client, requested scopes, and redirect destination. Do not set a consent-state cookie before approval. Keep credentials out of URLs, tool metadata, and logs; use short-lived credentials where supported and store them securely.
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Apply least privilege progressively: begin with the access needed for low-risk discovery or read operations, and grant additional permission only when a specific action requires it. Record enough request context and correlation identifiers to investigate tool calls and failures, without logging secrets or unnecessary personal data.
Control network, local-process, and agent risks
- Server-side request forgery: If a client or authorization service fetches a URL, restrict outbound destinations and network access rather than trusting a model-supplied address.
- Untrusted local code: Treat local server binaries and startup commands as executable software. Verify provenance, restrict permissions, and sandbox the process.
- Handle hijacking: Use random, expiring handles and bind them to the authenticated caller; do not treat a guessable identifier as authorization.
- Untrusted inputs and returned context: Validate tool inputs, constrain tool scope, and treat retrieved content as untrusted when the agent reasons over it.
- Prompt injection: Transport authorization alone does not prevent prompt injection. Use defense in depth across agent behavior, tool design, server-side policy, and downstream controls.
- Abuse and service degradation: Apply timeouts and rate limits, especially to expensive or externally visible operations, and monitor failures and unusual tool-call patterns.
How to test and operate the production system
Test the actual production endpoint and the compatibility of the host, client, server, and protocol versions you intend to deploy. A successful connection is not enough: verify that authorization, validation, failures, and consequential actions behave as designed.
- Exercise initialization and capability discovery, including the tools, resources, and prompts the server is intended to expose.
- Test valid inputs, malformed inputs, business-rule violations, missing permissions, expired or wrong-audience tokens, and downstream failures.
- Confirm that read, write, and destructive actions have distinct and appropriate controls, including approval where required.
- Verify timeouts, rate limits, logs, metrics, tracing, secret redaction, and incident-response correlation.
- Use a production secret-management facility, and document rollback steps and version compatibility expectations.
- Recheck current protocol deprecations and supported-client differences before release.
How to plan for MCP version changes
MCP is evolving, so pin the protocol and SDK versions used by each production integration and review release notes before upgrading. The 2026-07-28 release announcement says Dynamic Client Registration (DCR) is formally deprecated in favor of Client ID Metadata Documents. DCR remains for backward compatibility and is planned for removal in a future specification version.
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The same release describes authorization changes, including validating the issuer (iss) before redeeming a code and binding client credentials to the issuer that minted them. It notes that Tier 1 SDKs supported that revision at announcement time and that implementations relying on session identifiers could be affected. Check the relevant SDK documentation and test your integration before estimating or scheduling an upgrade.
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