You can design an MCP deployment with 25,000 actors in mind, but no single configuration—or published MCP capacity figure—proves that it will support that number. First define what “actor” means in your system, then configure authentication, authorization, state, hosting, and rate limits around the expected workload. Validate the deployed service with a load test before claiming it handles 25,000.
What does “25,000 actors” mean?
The number is not an operational target until you define what is being counted. “Actors” might mean registered identities, people using a service at the same time, running AI-agent processes, or simultaneous requests. Those measures produce different loads: 25,000 registered accounts do not necessarily generate 25,000 concurrent requests, while a smaller number of agents may produce bursts of tool calls.
Write down which interpretation you intend to test. If you mean concurrency, distinguish active connections from requests being processed at once and from requests arriving per second. If you mean users or agents, describe their expected request frequency and tool mix. The official MCP materials do not publish a benchmark or server-sizing recipe for 25,000 actors, so treat the figure as a target to validate rather than a confirmed capability.
How the current MCP protocol affects deployment
The MCP specification dated 2026-07-28 describes protocol requests as stateless: each request must contain the information needed to process it, and a server must not assume that conversation, client, or protocol context is available from an earlier request on the same connection. If your application needs state across requests, represent it with an explicit identifier supplied with each request.
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This request independence makes it reasonable to distribute requests across server instances without protocol-layer shared session storage. It does not make your application, databases, downstream services, or long-running jobs stateless. Those dependencies still need to tolerate the concurrency, availability requirements, and request patterns of your workload.
The release article for 2026-07-28 says that the version retires the initialization exchange and the Mcp-Session-Id header. It also describes routable operation headers, Mcp-Method and Mcp-Name, and cache metadata, ttlMs and cacheScope, for list and read results. These are version-specific details, not settings to copy blindly: check that the server and every connecting client implement compatible protocol behavior before relying on them. Older implementations may follow earlier behavior.
Choose a deployment model for the workload
OpenAI’s official MCP server deployment guidance identifies serverless hosting, containers, edge infrastructure, and traditional application infrastructure as options. It does not prescribe a provider or one universally best model. Compare candidates against how your tools run and where their dependencies live.
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| Decision factor | What to establish before choosing |
|---|---|
| Runtime and dependencies | Can the environment run the server’s language, packages, and required supporting services? |
| Streaming and latency | Does the hosting model support the response behavior your tools require, and what latency do you observe under your expected load? |
| Cold starts | Could scale-up or idle periods add unacceptable startup delay for your workload? |
| Network access | Can instances securely reach the APIs, data stores, or other downstream systems the tools need? |
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Local per-user stdio and a remote shared HTTP service are also different operating models. A local process runs in a user’s environment; a remote service centralizes deployment and must handle shared identity, quotas, and availability. Select according to how clients will connect and how you intend to operate the service—not simply because one model sounds more scalable.
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Configure identity, authorization, and credentials
Authentication establishes who or what is making a request; authorization determines what that identity may do. OpenAI’s deployment guidance calls for authentication on tools that read private data or act on a user’s behalf, and authorization on every request. Scope tool calls to validated credentials. Do not delegate access decisions to the model.
- Define the identity boundary. Decide how a validated user, account, or agent identity maps to permitted tools and data. The server—not model-generated text—must enforce that mapping.
- Validate the token for this MCP resource. MCP authorization security guidance says to verify that a token was issued for the MCP server and reject a token not intended for it.
- Use a separate upstream credential. If a tool calls another API, use the credential issued for that upstream service. Do not forward the inbound client token to it.
- Register exact redirect URIs. For OAuth flows, register and validate the exact redirect URI rather than accepting an unverified destination.
- Keep production secrets out of code and logs. Store credentials with the hosting platform’s secret-management system. Remove debug responses and ensure logs exclude access tokens and sensitive tool results; minimize personal data in logs.
These controls need to remain in place as the number of identities grows. A valid token alone does not establish that a caller is allowed to invoke every tool or access every record.
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Set rate limits and timeouts with an explicit scope
OpenAI advises timeouts and rate limits for tools that are expensive or have externally visible effects. AWS Prescriptive Guidance on MCP governance raises a key design choice: apply limits per MCP server, per tool, or using request attributes such as user or account. The sources do not give a universal numeric threshold, so set values from measured downstream capacity and business risk rather than importing a generic number.
Before enabling limits, document these choices:
- Scope: Identify whether each limit is global to the server, specific to a tool, or tied to an authenticated identity or account.
- Identity mapping: State which validated credential attribute selects the quota. Avoid relying on untrusted request text.
- Bursts: Decide what happens when many calls arrive together, not just what average request rate appears acceptable.
- Limit response: Define how the client learns a call was throttled and whether retrying is appropriate for that tool’s behavior.
- Timeout behavior: Choose timeouts based on tool cost and downstream behavior. A timed-out request should not be treated as proof that a downstream side effect did not occur.
For tools that make external changes, explicitly consider what a caller or client should do after a timeout or a rate-limit response. A retry may be harmless for a read and consequential for an action; the application’s behavior must account for that difference.
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Protocol-level request independence is not a promise that every request can be sent to any instance without preparation. If a workflow spans requests, include an explicit state identifier in the request context and make the associated state available wherever subsequent work may run. Do not depend on an instance remembering a previous request merely because the same client used it earlier.
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Review each tool for hidden state or shared dependencies: stored conversation data, job status, user-specific permissions, caches, and calls to other services. Decide which system owns each piece of state and how instances access it. MCP’s stateless protocol behavior can simplify routing, but it does not remove application-level consistency or downstream capacity constraints.
The 2026-07-28 release article describes cache metadata ttlMs and cacheScope for list and read results. Use such metadata only where it matches the data’s freshness and access rules, and verify that your particular server and clients support the version-specific behavior. Do not assume that caching makes private or user-specific results safe to share.
Verify the endpoint, then test the actual target
OpenAI’s deployment guide recommends exercising the production endpoint with MCP Inspector. Check discovery or initialization as applicable to your implementation, server instructions, tool names and schemas, annotations, authentication, returned results, and error behavior. Keep published tool names and schemas backward compatible when changing the service so existing clients do not break unexpectedly.
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Before calling the deployment a 25,000-actor configuration, write a test plan that describes the actual meaning of the target. Include:
- Whether 25,000 means identities, concurrent people, agent processes, or concurrent requests.
- The expected request mix across tools, request frequency, payload sizes, and any streaming duration.
- Downstream services and their own limits, since the MCP endpoint is only one part of the request path.
- Acceptable latency and error-rate targets, along with the period over which they must hold.
- How the test environment matches the deployed server, authentication, state stores, network paths, and downstream dependencies.
Then load-test the deployed stack with that model, monitor the endpoint and dependencies, and record the conditions and results. A test that demonstrates 25,000 registered accounts says little about 25,000 simultaneous requests; report precisely what was exercised. Neither the cited MCP specification nor the deployment guidance supplies capacity results for this figure.
Troubleshoot common configuration failures
| Symptom | Likely issue | What to check |
|---|---|---|
| Requests work on one instance but fail when distributed | Application state is held only in one process or the client and server rely on mismatched protocol behavior. | Check server/client versions and whether state spanning requests is addressed by an explicit identifier and available across instances. |
| Authentication succeeds but a tool cannot access an upstream API | The inbound token may have the wrong audience for the MCP server, or the upstream call may be using the wrong credential. | Validate the inbound token for the MCP resource and configure the separately issued upstream credential; do not forward the client token. |
| A user can call a tool but sees another user’s data or an unauthorized action succeeds | Authorization is missing, too broad, or not applied to each request. | Enforce server-side authorization on every request and scope calls to validated credentials. |
| Some callers hit limits while others do not, or a shared tool becomes overloaded | The quota scope or identity mapping may not match the intended policy. | Confirm whether limits apply per server, tool, user, or account, and test bursts as well as average traffic. |
| Clients fail after a protocol change | Client and server versions may implement different session or routing behavior. | Confirm exact versions and use only version-compatible protocol details and headers. |
| A test passes but production degrades at the target | The test may not represent real request mix, payloads, streaming duration, or downstream limits. | Compare recorded test conditions with the deployed workload and include all dependencies in the next test. |
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If your MCP workload includes website screenshots, ScreenshotNeo is a website screenshot API and MCP server. This example uses its screenshot API; it is not a configuration or capacity claim for a 25,000-actor MCP deployment. See the ScreenshotNeo API documentation for its request options.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webp
ScreenshotNeo can accept cookie or consent banners and remove known consent platforms, newsletter popups, and chat widgets before a shot; each step can be turned off. Bot checks, blank pages, timeouts, failed loads, and cache hits are not billed, and responses indicate the page verdict and billing status in headers. Its MCP server provides take_screenshot, get_page_info, and capture_pdf tools for AI agents. The free plan includes 1,000 screenshots a month without a card; paid plans start at $5 for 3,000.
Sign up free for 1,000 screenshots a month, with no card required.
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