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This guide is for developers deciding how to expose tools to an AI agent — MCP or CLI — and where to run the result. Verdict: weigh the token-budget cost of the setup first, hosting flexibility second, and don't commit to a host before you know what the server actually needs to run.

What changed and why it matters

blocks.ai published an article titled "MCP vs. CLI: 26,000 tokens burnt before your agent reads the prompt," putting a name on a problem developers have mostly been describing anecdotally: tool definitions loaded through MCP can eat into an agent's context window before it ever sees the user's actual instructions (source: blocks.ai, https://blocks.ai/blog/mcp-vs-cli-context-window-cost).

That framing changes what "best" means when you're picking an MCP setup. If tool-definition overhead competes with your prompt for context space, the deciding question isn't only "does it support the tools I need." It's how much of your budget the server's schema consumes before you get to use the model for anything, and how much control you have over trimming that down.

It also changes when the decision gets made. Teams used to pick a tool-integration approach once, early, and rarely revisit it. A published comparison of MCP against CLI on token cost gives you a concrete reason to re-check that choice on a schedule, the same way you'd re-check a cloud bill: not because the underlying protocol changed, but because the number of tools you've bolted onto an agent session tends to grow quietly until someone notices the context budget is gone.

None of this means MCP is the wrong choice, or that CLI automatically wins. It means the setup you're evaluating needs to be judged on its own token footprint, not on the reputation of the protocol it happens to use. That's the shift this guide is built around: fewer assumptions about which category is cheaper, more checking of the specific server and host in front of you.

What to look for

Buying criteria for an MCP setup, in priority order:

• Token footprint of the tool definitions. This is the cost the blocks.ai piece is naming — every tool schema the server exposes gets loaded into context, whether or not the agent uses it that turn. A server with a narrow, well-scoped tool set costs less per request than one that exposes everything by default.
• Hosting control. Can you see and change what the server process is doing, or are you stuck with whatever a vendor ships? Control matters more once you start trimming tool definitions to fix the problem above — you need to be able to redeploy your own changes.
• Documentation and maintenance activity. A server whose docs describe every tool and its input schema in one place is easier to audit for bloat than one you have to reverse-engineer by reading source.
• Pricing transparency. You should be able to find current compute pricing on the host's own site, not estimate it from a blog post or a sales call.
• Integration with your existing stack. A server that fits your current deployment pipeline avoids adding a second, unrelated ops process to maintain.
• Scoping and access control. A tool server that lets you expose a subset of tools per environment or per caller keeps both the token footprint and the blast radius of a misconfigured tool smaller than one that's all-or-nothing.
• Update cadence. A server you can version and roll back matters once you start trimming or rewriting tool schemas to fix a token problem — you want to be able to undo a change that broke something without redeploying from scratch.

Rank these for your own situation before you shop. A team running one lightweight internal tool cares less about token footprint than a team wiring a dozen tools into a single agent session, and a team with one developer cares less about scoping than a team where multiple callers share the same server.

Recommended options

These are the three hosting shapes an MCP server typically runs on. None of them is a single fixed product — match the shape to your criteria above, then check current specs and pricing on the provider's own site before deploying. Team size and how often you expect to change the tool schema are usually better predictors of the right shape than raw traffic volume: a solo developer iterating on tool definitions daily wants fast, direct control over deploys more than they want autoscaling.

Self-hosted on a VPS

Best for: teams that want full control over the server process, including the ability to strip unused tool definitions down to size and redeploy on their own schedule.

Skip it if: you don't want to own OS patching, process supervision, and uptime monitoring yourself. A VPS gives you control in exchange for operational work; if nobody on the team wants that work, it's the wrong shape.

Managed container or app platform

Best for: teams that want to push a Dockerized MCP server and let the platform handle restarts, scaling, and TLS, rather than configuring those by hand.

Skip it if: you need kernel-level control or non-standard networking the platform doesn't expose. Managed platforms trade some of the control a VPS gives you for less operational overhead.

Serverless or function-based hosting

Best for: an MCP server with spiky or infrequent traffic, where paying for idle compute doesn't make sense.

Skip it if: your server needs persistent state or a long-lived connection — most serverless platforms are built around short-lived invocations and will fight you on anything that needs to stay warm.

None of these three shapes changes the token math from the section above on its own. What they change is who owns the fix when the math is bad: on a VPS, you do, immediately; on a managed platform, you're often waiting on the platform to expose a setting; on serverless, you're usually rewriting the function.

That's also why the hosting decision belongs after the criteria decision, not before it. If you already know you need to trim tool definitions per environment, that points toward a shape where you control the deploy — a VPS or a container platform you can push to directly — rather than one where a vendor's default configuration decides what ships.

Whichever shape you pick, the token-footprint criterion from the section above still applies — hosting doesn't fix an oversized tool schema, it just decides who's responsible for fixing it.

Common mistakes

Sizing the host to the wrong number. Buyers often size compute to the MCP server's idle footprint, not its footprint under a full tool-call load. [See DigitalOcean's current droplet and App Platform pricing](See current pricing at the merchant) so you're matching a plan to your server's actual memory and CPU needs, not a guess, before you commit to a tier.

Assuming CLI is automatically cheaper. The blocks.ai piece specifically frames this as a comparison worth checking, not a settled fact you can assume applies to your own setup. Read the actual tool schema your MCP server loads before deciding CLI would save you tokens — the difference depends on how many tools you expose and how verbose their definitions are, not on the protocol name alone.

Picking a host before deciding what the server needs. Locking in a VPS, a managed platform, or serverless functions before you know whether the server needs persistent state, how often it's called, and how large its tool schema is means re-migrating later. Decide the operational shape from the criteria in this guide first, then shop hosts.

Treating documentation gaps as acceptable. If you can't find a clear list of every tool a server exposes and its input schema, you can't audit its token footprint — which is the first criterion on this list. Don't take "it works" as a substitute for "we know what it costs."

Skipping the pricing check until deployment. Compute pricing changes, and plan tiers get renamed or resized. Confirm current pricing on the provider's own site at the point you're ready to deploy, not from a screenshot or an old thread.

Exposing every tool by default because it's easier. It's faster to ship an MCP server with every available tool switched on than to scope it per environment. It's also the single fastest way to burn through the context budget the blocks.ai article is describing, since every exposed tool's schema loads whether or not that session ever calls it.

Comparing protocols instead of comparing setups. "MCP versus CLI" is a useful framing for a headline, but the actual cost you'll pay depends on how a specific server author scoped their tool definitions, not on the protocol name printed on the box. Two MCP servers can have wildly different token footprints; judge the one you're about to deploy, not the category it belongs to.

The decision that matters here isn't MCP versus CLI in the abstract — it's whether the specific MCP setup you're evaluating keeps its tool-definition overhead small enough to leave room for your actual prompt, and whether the host you pick lets you fix that if it doesn't.