The Tool Desk
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Define what “25,000 actors” means
Capacity planning changes completely depending on what the number counts. Write a one-sentence workload definition before choosing infrastructure:
- Registered actors: identities that may call the server, but not necessarily active at once.
- Concurrent actors: people or agent processes actively holding connections or making requests at the same time.
- Concurrent requests: in-flight tool calls. This is usually the most important number for CPU, memory and downstream capacity.
- Long-running actors: clients that keep streams open or start jobs that continue after the initial request.
For example, “25,000 registered identities, at most 1,000 in-flight requests, with a 60-second p95 tool latency target” is an actionable specification. “25,000 actors” alone is not. Record request frequency, tool mix, payload sizes, streaming duration, authentication provider, downstream quotas and acceptable error rates alongside the headline number.
Align the MCP version before configuring anything
The MCP specification dated 2026-07-28 describes MCP as stateless at the protocol layer: every request carries the information needed to process it, and a server must not infer conversation, client or protocol context from an earlier request on the same connection. State that spans requests therefore needs an explicit identifier supplied with each request and stored or retrieved by the application.
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The associated release article describes two important changes: the initialization exchange and Mcp-Session-Id header are retired, and requests can be routed between instances without protocol-level shared session storage. It also describes routable Mcp-Method and Mcp-Name headers, plus ttlMs and cacheScope metadata for list and read results. These details are version-sensitive. Confirm that your server, client and gateway implement the same specification revision before copying them into a deployment.
Older implementations may still expect the earlier session behavior. A mixed fleet can fail in ways that look like random authentication, routing or discovery errors, so pin versions, roll out one compatible revision at a time and keep a rollback image for the previous protocol behavior.
Choose an architecture that can be distributed
| Decision | When it fits | Scale concern |
|---|---|---|
| Local per-user stdio | A desktop client launches a private server process. | Each process is isolated; fleet-wide quotas, updates and observability are harder to coordinate. |
| Remote shared HTTP service | Many users or agents call one managed endpoint. | Requires an ingress layer, identity mapping, rate limits, centralized logs and horizontally safe state. |
| Serverless runtime | Burst traffic and short, independent tool calls. | Check dependency support, cold starts, streaming limits, execution duration and network access. |
| Containers or traditional application infrastructure | Predictable dependencies, long-lived streams or sustained traffic. | You own capacity, rolling deployment, autoscaling, health checks and failure recovery. |
| Edge runtime | Low-latency request handling near users and compatible downstream services. | Verify runtime APIs, data residency, connection behavior and access to private networks. |
No single hosting provider is prescribed. Compare runtime and dependency support, streaming behavior, cold-start and request latency, network access to downstream services, data residency, secret management, logging and tracing, alerting, and rollback/versioning support.
Make application state explicit
Protocol statelessness does not make your business logic stateless. Shopping carts, permissions, pagination cursors, approval workflows and long-running jobs still need storage. Put an explicit key in each request or in a signed identity context, then retrieve state from a shared store available to every server instance.
- Use an immutable actor or tenant identifier for authorization scope.
- Use a separate request or job identifier for idempotency and tracing.
- Store conversation or workflow data in a database, cache or queue that all instances can reach.
- Set expiration and deletion rules for temporary state and personal data.
- Do not assume a load balancer will return an actor to the same instance; avoid sticky sessions unless a specific legacy dependency requires them.
A provider-neutral configuration model can look like this. It is a design template, not a universal MCP file format; map the fields to your server framework and deployment system:
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{
"protocolVersion": "2026-07-28",
"transport": "https",
"state": {
"requestKey": "request_id",
"actorKey": "actor_id",
"store": "shared-database-or-cache"
},
"auth": {
"resourceAudience": "https://mcp.example.com",
"upstreamCredential": "separate-secret-reference"
},
"limits": {
"scope": "identity-and-tool",
"burstPolicy": "reject-or-queue",
"values": "set-from-load-test"
},
"observability": {
"traceKey": "request_id",
"redactTokens": true
}
}Authenticate and authorize every request on the server
Authentication belongs in the MCP server or a trusted gateway, not in the model. The server should:
- Validate the credential, issuer, signature, expiry and required claims.
- Validate that the token was issued for this MCP resource. A token intended for another API must be rejected even if it is otherwise valid.
- Map the validated subject to an actor, tenant and permitted tools.
- Authorize the specific operation and resource on every request, including reads.
- Pass only the minimum authorized identity context to tool code.
If a tool calls an upstream API, use a separately issued upstream credential. Do not forward the inbound client token to that API. In OAuth flows, register and validate exact redirect URIs; do not accept arbitrary redirect destinations supplied at runtime.
Keep production credentials in the hosting platform’s secret manager. Remove debug responses, redact access tokens and sensitive tool results from logs, and minimize personal data in traces. Give operators a way to revoke an actor or rotate credentials without rebuilding every server instance.
Design rate limits around cost and identity
There is no universal MCP number that makes 25,000 actors safe. Choose limits from measured tool cost and downstream quotas. Document all of the following:
- Scope: per MCP server, per tool, per actor, per account or a combination.
- Identity mapping: which validated claim determines the quota key, and how service accounts differ from human users.
- Burst behavior: reject with a clear retry response, queue work, or shed low-priority requests.
- Accounting: whether streaming duration, payload size or downstream calls consume quota in addition to request count.
- Failure behavior: what the client sees when a limit is reached and how operators are alerted.
Expensive or externally visible tools deserve tighter, tool-specific controls. A cheap metadata read may have a different budget from a tool that sends messages, changes records or starts a long job. Enforce authorization before consuming an expensive downstream operation.
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Implement routing, health and graceful failure
Ingress and routing
Terminate TLS at a controlled gateway or at the service, preserve the authenticated identity and request identifier, and route requests to any healthy instance. If your implementation uses the 2026-07-28 routable headers, ensure the gateway preserves Mcp-Method and Mcp-Name rather than normalizing them away. Never route solely on an untrusted actor header.
Timeouts and cancellation
Set separate limits for connection establishment, request processing, downstream calls and total streaming duration. Propagate cancellation to databases, queues and third-party APIs. A client disconnect should not leave an unbounded job consuming capacity; move legitimate long work to a durable job system and return a job identifier.
Health and deployment
Use a readiness check that proves the instance can serve the negotiated protocol and reach required dependencies, not merely that its process is alive. Drain an instance before termination so active streams can finish or be cancelled cleanly. Deploy compatible server and client versions together, observe discovery and tool-call errors, and keep the prior image available for rollback.
Verify the production endpoint
Before exposing the endpoint to thousands of actors, exercise it with MCP Inspector against the real hostname and authentication path. Verify:
- the applicable initialization or discovery behavior for your protocol version;
- server instructions, tool names, input schemas and annotations;
- authentication failures, authorization denials and successful results;
- timeouts, cancellation and rate-limit responses;
- redaction in logs and traces; and
- backward compatibility for published tool names and schemas.
Test through the same gateway, identity provider, DNS, certificates and downstream networks used in production. A local successful call does not validate the distributed path.
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Prove the 25,000-actor target with a workload test
The target becomes a claim only after a workload-specific test. Build a test plan that states:
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- How many identities exist and how many are active concurrently.
- The request rate and tool mix for each actor class.
- Payload sizes, cacheability, streaming duration and job completion behavior.
- Database, queue, identity-provider and third-party API quotas.
- Success-rate, p50/p95/p99 latency, timeout and cancellation objectives.
- Per-instance CPU, memory, connection pools, event-loop or worker saturation, and autoscaling delay.
- What happens during an instance failure, downstream slowdown, credential outage or traffic burst.
Run the test against the deployed topology, ramp traffic gradually, then repeat with a failed instance and a constrained downstream dependency. Record the exact server and client versions, region, runtime sizes, test duration and dataset. Without those conditions, “supports 25,000” is not a reproducible statement.
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Troubleshoot common failures
Requests fail after a rolling deployment
Likely cause: clients and instances implement different protocol revisions, especially around initialization or session headers. Fix: pin and verify versions, drain old instances, and deploy a compatible client/server pair.
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Authentication succeeds but a tool is denied
Likely cause: authentication was mistaken for authorization, or the token audience is for another resource. Fix: validate the resource audience, map the subject to its tenant and permissions, and authorize the exact tool and object.
Calls work on one instance but fail after routing
Likely cause: state lives only in process memory. Fix: carry an explicit actor, request or job identifier and move shared state to a store reachable by every instance.
Upstream API returns unauthorized
Likely cause: the inbound MCP token was forwarded upstream. Fix: obtain and store a credential issued for that upstream resource, then apply least-privilege scopes.
Latency spikes during bursts
Likely cause: unbounded concurrency, cold starts, exhausted connection pools or a downstream quota. Fix: measure each layer, cap concurrency, queue suitable work, set timeouts and apply identity- and tool-aware limits.
Logs expose private data
Likely cause: debug payloads or authorization headers are logged by the application or gateway. Fix: redact tokens and sensitive tool results, minimize fields, and review logs from both layers.
Operational checklist
- Define the actor and concurrency model in writing.
- Align protocol, client and server versions.
- Use explicit cross-request identifiers and shared state where needed.
- Validate token audience and authorize every tool call server-side.
- Use separate upstream credentials.
- Document rate-limit scope, identity mapping and burst behavior.
- Set timeouts, cancellation, readiness checks and graceful draining.
- Protect secrets and redact logs.
- Verify the real endpoint with MCP Inspector.
- Load-test the deployed topology and publish the test conditions before claiming 25,000-actor support.
Frequently Asked Questions
Do I need sticky sessions for the current MCP protocol?
Not at the protocol layer described by the 2026-07-28 specification. You may still need shared application state or a job store; solve that dependency explicitly rather than relying on affinity.
Which hosting provider should I use?
The available guidance does not prescribe one. Select serverless, containers, edge or traditional infrastructure by checking runtime support, streaming, latency, networking, residency, secrets, observability and rollback requirements.
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It can, but that is usually a poor cost and risk model. Set scope deliberately and consider separate budgets for identities, accounts and tools with different downstream effects.
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