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What does MCP standardize—and what remains your responsibility?
MCP is an open standard for connecting AI applications to external systems, including data sources, tools, and workflows. Its purpose is to let a host discover and use capabilities exposed by servers through a common protocol. It does not prescribe which model the host uses, how the agent reasons or orchestrates work, or how an enterprise sets its access policy. Those decisions remain with the application and organization. See the MCP introduction.
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The architecture has three roles: a host application, an MCP client created by that host for each server connection, and one or more MCP servers. The client and server communicate using a data layer based on JSON-RPC and a transport layer that establishes the communication channel and handles transport-specific authorization. The MCP architecture overview describes these layers and roles.
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Tools, resources, and prompts are different capabilities
| Primitive | What it provides | Enterprise design implication |
|---|---|---|
| Tools | Executable functions a client can discover and call. | May enable reads or consequential writes. Authorize every invocation and validate inputs against schemas and business rules. |
| Resources | Context data a server makes available to clients. | Control which data is exposed and to which authenticated principals; treat returned content as untrusted input to the agent. |
| Prompts | Reusable interaction templates provided by a server. | They can shape how an interaction is initiated, but do not replace server-side access checks or policy enforcement. |
These primitives and the client’s capability-discovery model are described in the MCP architecture documentation. A tool annotation such as read-only or destructive can help describe intended behavior, but it is not an authorization mechanism. OpenAI’s MCP server guidance likewise calls for authorization on every request rather than relying on the model to decide what a user may access.
Stateless protocol does not mean stateless business workflow
The specification says, “MCP is a stateless protocol: all the information needed to process a request is contained in the request itself.” That is a protocol-level statement, not a requirement that an enterprise workflow have no state. If a workflow needs a handle to refer to an object or ongoing process, represent that state explicitly on the server, make handles random and expiring, bind them to the authenticated principal, and authorize each later request. A process or open connection is not proof of a user’s identity or a durable conversation boundary. See the architecture overview and MCP security best practices.
Should the server run locally or remotely?
Local stdio and remote Streamable HTTP are different operating and trust-boundary choices, not interchangeable deployment labels. Local stdio runs a server process alongside the host and exchanges messages over standard input and output. Remote Streamable HTTP connects over HTTP and supports streaming. Neither is universally right; select the pattern that fits the clients, data, network, and team that will operate the server.
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|---|---|---|
| Where it runs | Alongside the host, commonly on a managed workstation or within a tightly controlled local process. | On managed infrastructure reachable by clients over HTTP; suitable when a server must serve multiple clients. |
| Primary trust concern | Server code and startup configuration execute in the local machine’s security context and may inherit machine-level access. | Network reachability, authentication, service exposure, and the controls around a shared endpoint. |
| Operations to plan | Trusted provenance for binaries and commands, sandboxing, permissions, and local update practices. | Availability, rate limits, logging, secrets, data residency, monitoring, patching, and rollback. |
| Transport consideration | Standard input/output; avoid treating the process or its connection as the identity boundary. | HTTP with streaming support; design the authorization and network path for the service. |
Local servers can be appropriate for developer workflows or tightly managed local processes, but untrusted startup commands or binaries can compromise the host. Remote hosting can use serverless, containers, edge, or traditional application infrastructure; the suitable option depends on runtime, streaming and latency needs, network access, residency, secret management, observability, rollback, and versioning. These trade-offs are covered in the MCP architecture overview, security best practices, and OpenAI’s deployment guidance.
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Use a decision sequence, not a default transport
- Set the trust boundary. Decide whether the server should run only on a managed endpoint or be reachable as a shared service. Identify which data and downstream systems each boundary can reach.
- Check client and server support. Confirm that the host, MCP client, and server support the transport you intend to deploy. Do not assume older HTTP+SSE examples reflect current guidance.
- Map identity to downstream permissions. Establish how the signed-in user or calling service is represented at the server and what credential the server uses with each downstream API.
- Evaluate operations. Assign responsibility for patching, availability, secrets, monitoring, incident response, and rollback before exposing the server to production users.
- Account for constraints. Compare streaming and latency needs, network reachability, data residency, and protocol and SDK compatibility against the real deployment environment.
The older HTTP+SSE transport is marked for retirement in the project’s 2026-07-28 specification release announcement. Check current support in the specific clients and SDKs you use rather than selecting a transport from an old example.
How should an enterprise secure an MCP server?
Make the server an enforcement point, not a transparent conduit. For every request, authenticate the caller, authorize the requested operation against that caller’s permissions, validate the input, and apply business policy. Where a consequential action changes records, sends communications, or triggers an external effect, require explicit user approval that clearly describes what will happen. Do not treat a model decision, advertised schema, or tool annotation as a substitute for these controls. OpenAI’s server guidance calls for request-level authorization and validation.
For HTTP authorization, validate the right token for the right server
Follow the current MCP authorization specification for HTTP-based authorization. Use resource-metadata discovery and OAuth security practices; clients must use PKCE and verify PKCE support, and include the resource parameter. Servers must validate that an incoming access token was issued for that server. Use HTTPS for authorization endpoints and secure redirect URIs. The MCP server should use a separately issued upstream credential when it calls another API: never forward the MCP client’s token to an upstream service or accept a token intended for a different resource. Consult the MCP authorization security considerations.
Protect consent, redirects, and authorization state
A proxy server can become a confused deputy if it uses a shared or static client identity, handles consent poorly, or accepts an insufficiently verified redirect. Where consent applies, obtain approval for the specific client and show the client, requested scopes, and redirect destination. Validate redirect URIs exactly; protect state against cross-site request forgery and replay; and do not establish a consent-state cookie before approval. The MCP security guidance and authorization specification cover these safeguards.
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Limit credentials, tool scope, and network exposure
- Grant the minimum permissions needed for each operation. Begin with low-risk discovery or reads and require additional authorization for actions that need it.
- Keep credentials out of URLs, tool metadata, and logs. Use secure storage and short-lived credentials where supported; use a production secret-management facility for service credentials.
- Separate reads from writes where practical, give tools narrow purposes and schemas, and apply policy checks at both the MCP server and downstream service.
- Set timeouts and rate limits, especially for expensive or externally visible operations. Monitor failures and tool calls using correlation identifiers without logging secrets or unnecessary personal data.
- Control outbound destinations when clients or authorization servers fetch URLs to reduce server-side request forgery risk. Restrict local server permissions and sandbox trusted code.
- For any server-side workflow handle, use an unpredictable, expiring value bound to the authenticated user; authorize each use rather than trusting possession of a handle alone.
These controls address authorization, credential handling, server-side request forgery, local process risk, and handle hijacking identified in the MCP security best practices. Tool inputs and returned context should also be treated as untrusted. Prompt injection is a broader agent threat: transport authorization does not solve it, so apply defense in depth in the host, tools, and downstream systems.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should the build and release process test?
Build the server around specific capabilities and enforce policy before connecting it to a model. A practical sequence is to define the data and actions, implement narrow server-side controls, validate the end-to-end identity path, then test the exact production endpoint and supported client versions.
- Inventory capabilities. List every exposed resource and tool. Classify operations as read, write, or destructive, identify sensitive data, and document their downstream effects.
- Define authorization mappings. Specify how each authenticated user or service maps to downstream permissions. Make the server enforce the mapping on every request, including repeated requests that use workflow handles.
- Choose the deployment pattern. Select local stdio or remote Streamable HTTP based on the trust boundary, runtime, streaming and latency needs, network constraints, residency, and operational ownership.
- Implement credential separation. For HTTP authorization, implement discovery, PKCE, resource and audience validation, secure redirects, and a distinct upstream credential flow. Keep secrets out of logs and metadata.
- Test protocol behavior. Pin protocol and SDK versions, then test initialization, discovery, advertised schemas, valid and invalid inputs, authorization failures, timeouts, and error handling against the production endpoint.
- Exercise consequential paths. Verify that unauthorized writes are denied server-side and that the approval step makes the intended change clear before execution. Test rate limits and downstream failure behavior.
- Operate and recover. Establish metrics, tracing, incident response, credential rotation, patching, version compatibility, and a rollback path before rollout.
- Review changes before release. Check current protocol deprecations and supported-client differences, then repeat compatibility and authorization tests when upgrading.
This sequence reflects controls in the MCP authorization guidance, security best practices, OpenAI’s server deployment guidance, and the OpenAI Agents SDK MCP documentation.
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MCP evolves, so pinning a protocol and SDK version is a release-management decision, not a one-time setup task. The project’s 2026-07-28 specification 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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That release also marks Roots, Sampling, Logging, and legacy HTTP+SSE as deprecated, describing at least a twelve-month compatibility period for those items in that announcement. These are release-specific statements, not assurances about later revisions or every client implementation. Check the current specification and the support status of the clients and SDKs in your deployment before planning a migration.
The announcement also 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 migration could affect implementations relying on session identifiers. Review the relevant SDK documentation and test your own identity and session flows before estimating upgrade work.
A safe upgrade process is to record the protocol and SDK versions in use, review release notes and deprecations, test initialization and discovery against each supported client, and run authorization and failure-path tests in a staging environment. Roll out with a rollback plan, and avoid depending on a deprecated feature without a migration owner and date.
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