How to Cut Your MCP Token Costs in Half
Your MCP servers are eating your context window and your budget. Here's a practical guide to auditing token usage, building lean stacks, and saving money.
A developer posted their Claude Code bill last month. They started a coding session expecting to spend a couple of dollars. They ended the day $47 poorer. The culprit was not some runaway loop or hallucinated refactor. It was their MCP setup: 4 servers exposing 167 tools, consuming roughly 60,000 tokens before they typed a single character.
That is not an edge case. It is what happens when you add MCP servers without thinking about what each one costs you in context window space -- and by extension, in actual money. If you are running MCP servers with any paid AI model, this is a problem worth solving. The good news: solving it is straightforward, and the savings are immediate.
Why MCP Tokens Hit Your Wallet
Every time you start a session with MCP servers enabled, every tool from every server gets injected into your context window. Not just the tool names. The full definitions -- names, descriptions, complete JSON schemas, every parameter with its type and constraints. Whether you use those tools or not, you pay for them.
Here is the math that matters. Claude charges $3 per million input tokens. GPT-4o charges $2.50 per million. These are small numbers until you realize how quickly MCP overhead accumulates.
A 60K token MCP overhead costs you approximately $0.18 per prompt on Claude -- before you even type anything. That is just the tool definitions being loaded into context. Your actual prompt, the code in context, the conversation history, and the model's reasoning all add more tokens on top of that.
Now scale it. A productive developer sends 80 to 120 prompts per day. At 100 prompts per day with 60K tokens of MCP overhead:
- Claude (Sonnet): $0.18 x 100 = $18/day just in MCP overhead
- GPT-4o: $0.15 x 100 = $15/day just in MCP overhead
That is $360 to $540 per month in token costs that produce zero value. You are paying for tool definitions to sit in context, not for work being done. Cut that overhead in half and you save $180 to $270 per month. Cut it by two-thirds and the savings approach the cost of a junior developer's daily coffee budget.
The financial argument is clear. But there is a second cost that does not show up on your bill: context window saturation. A 200K token context window with 60K tokens of MCP overhead leaves you with 140K tokens for actual work. On a 128K window model, you are left with 68K. On Claude Code, users have reported hitting 67K tokens from MCP definitions alone, leaving barely half the window for code, conversation, and reasoning.
Client developers know this is a problem. Cursor caps MCP tools at 40. GitHub Copilot caps at 128. These limits exist because unbounded tool counts degrade both performance and cost-efficiency.
Audit Your Current Setup
Before you can cut costs, you need to know what you are spending. Here is a five-minute audit.
Step 1: Count your servers and tools. Open your MCP configuration file and list every server you have enabled. For each one, note how many tools it exposes. Most servers list this in their documentation, or your client may show it in the MCP panel.
Step 2: Estimate your token overhead. Multiply your total tool count by 515. That is the average token cost per tool based on real-world measurements across popular MCP servers. The number varies -- some tools have simple schemas and cost 200 tokens, others with complex nested parameters can cost 800 or more -- but 515 is a reliable average for planning.
Step 3: Calculate your context window percentage. Divide your estimated token overhead by your model's context window size. This is the single most important number in your MCP setup.
Here is an example:
| Setup A | Setup B | Setup C | |
|---|---|---|---|
| Servers | 3 | 6 | 10 |
| Total tools | 25 | 65 | 140 |
| Estimated tokens | ~12,875 | ~33,475 | ~72,100 |
| % of 200K window | 6.4% | 16.7% | 36.1% |
| Cost per prompt (Claude) | $0.04 | $0.10 | $0.22 |
Setup A is lean and efficient. Setup B is starting to get heavy. Setup C is actively burning money and degrading model performance. If your audit puts you in Setup B or C territory, keep reading.
Five Strategies to Cut Token Costs
Strategy 1: The "Last Used" Test
Go through each MCP server in your config and ask one question: when did I last use this? If the answer is more than a week ago, remove it.
MCP servers take 30 seconds to add back. There is no reason to carry the daily token cost of a server you use once a month. The Puppeteer MCP server you added for that one scraping task three weeks ago? Remove it. The database server for a side project you have not touched? Remove it. That experimental server you installed to try out and then forgot about? Definitely remove it.
This single step typically eliminates 20 to 40 percent of a developer's MCP token overhead. It costs nothing and takes two minutes.
Strategy 2: Quality Over Quantity
Not all MCP servers are created equal. Some expose 8 focused tools that cover a workflow completely. Others expose 40 tools, half of which overlap with each other or with tools from other servers.
When choosing between servers that cover similar ground, pick the one with fewer tools but better coverage of what you actually need. A server with 8 well-designed tools costs you roughly 4,120 tokens. A server with 30 tools that does the same job costs 15,450 tokens. That is an 11,330 token difference -- and you get nothing extra for it.
Common overlaps to watch for:
- File system access: Multiple servers often include file read/write tools. You usually need only one, and your AI client likely has built-in file access already.
- Web browsing: Playwright MCP, Browserbase MCP, and Puppeteer MCP all control browsers. Pick one.
- Documentation search: Context7, web search servers, and some framework-specific servers all provide documentation access. Consolidate.
- Database access: A Supabase MCP server covers everything a generic Postgres server does, plus more. Running both is pure waste.
Strategy 3: Use Curated Stacks
Assembling an MCP setup server by server is like grocery shopping without a recipe. You end up with ingredients that do not go together, duplicates, and things you never use.
Curated stacks solve this by design. Each stack on stackmcp.dev is built for a specific workflow, with servers that complement each other without overlapping. The token budgets are calculated, the overlap is eliminated, and the configuration is pre-generated for your client.
Instead of spending an afternoon researching which 5 of the 50+ available MCP servers actually work well together, start with a stack and customize from there. It is the fastest path to a lean setup.
Strategy 4: Use the Token Calculator
Estimating token costs with mental math works, but seeing real numbers changes behavior. The Token Calculator on stackmcp.dev lets you select specific servers and immediately see the total token count, the percentage of your context window consumed, and the estimated cost per session.
This is particularly useful when you are deciding whether to add one more server. That "it is just one more server" instinct is how setups bloat from 15K tokens to 60K tokens. The calculator makes the marginal cost visible before you commit to it.
Strategy 5: Match Your Model to Your Stack
If you genuinely need a heavy MCP setup -- maybe you are running a complex full-stack workflow with database, deployment, monitoring, and design tools -- consider which model you are using.
A 60K token MCP overhead is 30% of Claude's 200K context window. That same overhead is only 6% of Gemini's 1M token context window. The trade-off is not just about context size -- model capabilities, speed, and pricing all differ -- but if your workflow demands many tools, a larger context window means your MCP overhead takes a proportionally smaller bite.
| Model | Context Window | 60K Overhead As % | Remaining for Work |
|---|---|---|---|
| Claude Sonnet | 200K | 30% | 140K |
| GPT-4o | 128K | 46.9% | 68K |
| Gemini Pro | 1M | 6% | 940K |
This does not mean you should always use the largest context model. It means your model choice and your MCP setup should be considered together, not independently.
Three Stacks Compared: The Real Cost
To make the impact concrete, here are three example stacks at different weight classes, with their real costs over a day of productive coding (100 prompts on Claude Sonnet).
| Lean Stack | Balanced Stack | Heavy Stack | |
|---|---|---|---|
| Servers | 3 | 5 | 8 |
| Example servers | Context7, Sequential Thinking, Filesystem | Supabase, GitHub, Playwright, Context7, Sentry | Supabase, GitHub, Stripe, Playwright, Vercel, Sentry, Figma, Context7 |
| Total tools | ~12 | ~30 | ~80 |
| Token overhead | ~5,000 | ~15,000 | ~40,000 |
| % of 200K window | 2.5% | 7.5% | 20% |
| Cost per prompt | $0.015 | $0.045 | $0.12 |
| Cost per 100 prompts | $1.50 | $4.50 | $12.00 |
| Monthly (weekdays) | $30 | $90 | $240 |
The difference between a lean stack and a heavy stack is $210 per month in MCP overhead alone. That is not the total cost of using the AI model -- it is just the cost of tool definitions sitting in context. Your actual prompts, code, and conversation add more on top.
Notice that the lean stack consumes only 2.5% of the context window. That leaves 97.5% for actual work. The heavy stack consumes 20%, which is manageable but starts to matter on long coding sessions where conversation history accumulates.
Use the Stack Builder
If you want to see these trade-offs in real time, the Stack Builder on stackmcp.dev lets you add and remove servers while watching the token count, context window percentage, and estimated cost update live.
It is the fastest way to find the sweet spot for your specific workflow: the smallest set of servers that covers everything you actually need, with nothing extra. Add a server, see the cost jump. Remove one, see it drop. The visual feedback makes it much easier to make deliberate choices about what earns its place in your stack.
The Bottom Line
MCP servers are powerful. They turn your AI assistant from a code completion tool into something that can interact with your entire development infrastructure. But that power has a measurable cost, and most developers are paying far more than they need to.
The target is simple: keep your MCP token overhead under 10% of your context window. On a 200K context window, that is 20K tokens, or roughly 40 tools. That is enough for 3 to 5 well-chosen servers that cover a complete workflow without waste.
Three things to do right now:
- Run the audit. Count your servers, multiply tools by 515, and check what percentage of your context window you are burning on tool definitions.
- Cut the dead weight. Remove any server you have not used in the past week. You can always add it back in 30 seconds.
- Use the tools. The Token Calculator and Stack Builder on stackmcp.dev exist specifically to help you make these decisions with real numbers instead of guesses.
Your AI assistant gets smarter, faster, and cheaper when you give it fewer tools and more room to think. A lean MCP stack is not a limitation -- it is the most effective setup you can run.