How to Cut Your MCP Token Costs in Half

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.

TL;DR: Every MCP tool costs ~500 tokens of context window space, loaded on every prompt whether you use it or not. A bloated setup of 10 servers can burn $240/month in overhead alone. Keep your MCP token overhead under 10% of your context window (~40 tools). Audit with the formula: total tools x 515 = token overhead. Cut servers you haven't used in a week.

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, 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. Claude charges $3 per million input tokens. A 60K token MCP overhead costs approximately $0.18 per prompt -- before you type anything. Scale that to 100 prompts per day:

• 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.

The second cost 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. Client developers know this is a problem -- Cursor caps MCP tools at 40, GitHub Copilot caps at 128.

Token Budget Breakdown

pie title Context Window Usage (Bloated Setup - 200K)
    "MCP Tool Definitions" : 60
    "Conversation History" : 40
    "Code Context" : 50
    "Model Reasoning" : 30
    "Available Headroom" : 20

In a bloated setup, MCP tool definitions consume the largest single share of your context window -- more than your actual code or conversation. A lean setup flips this ratio entirely.

Audit Your Current Setup

A five-minute audit before you can cut costs.

Step 1: Count your servers and tools. Open your MCP configuration file and list every server. Note how many tools each one exposes.

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.

Step 3: Calculate your context window percentage. Divide your estimated token overhead by your model's context window size.

Setup A is lean. Setup B is getting heavy. Setup C is actively burning money. If your audit puts you in B or C territory, keep reading.

Five Strategies to Cut Token Costs

Strategy 1: The "Last Used" Test

Go through each MCP server and ask: 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. This single step typically eliminates 20 to 40 percent of token overhead.

Strategy 2: Quality Over Quantity

When choosing between servers that cover similar ground, pick the one with fewer tools but better coverage. A server with 8 well-designed tools costs ~4,120 tokens. A server with 30 tools doing the same job costs ~15,450 tokens.

Common overlaps to eliminate:

• File system access: Multiple servers often include file read/write tools. Your AI client likely has built-in file access already.
• Web browsing: Playwright MCP, Browserbase MCP, and Puppeteer MCP all control browsers. Pick one -- see our Playwright vs Puppeteer comparison.
• Documentation search: Context7, web search servers, and framework-specific servers all provide docs access. Consolidate.
• Database access: A Supabase MCP server covers everything a generic Postgres server does, plus more. Running both is pure waste -- see our database MCP comparison.

Strategy 3: Use Curated Stacks

Assembling an MCP setup server by server is like grocery shopping without a recipe. 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. Token budgets are calculated and overlap is eliminated.

Strategy 4: Use the Token Calculator

The Token Calculator on stackmcp.dev lets you select specific servers and immediately see the total token count, context window percentage, and estimated cost per session. This makes the marginal cost of "just one more server" visible before you commit.

Strategy 5: Match Your Model to Your Stack

A 60K token MCP overhead is 30% of Claude's 200K context window. That same overhead is only 6% of Gemini's 1M token window.

Your model choice and your MCP setup should be considered together, not independently.

Three Stacks Compared: The Real Cost

Real costs over a day of productive coding (100 prompts on Claude Sonnet):

The difference between lean and heavy is $210 per month in MCP overhead alone. The lean stack consumes 2.5% of context, leaving 97.5% for actual work.

The Bottom Line

Keep your MCP token overhead under 10% of your context window. On a 200K window, that is 20K tokens, or roughly 40 tools -- enough for 3 to 5 well-chosen servers.

Three things to do right now:

1. Run the audit. Count your servers, multiply tools by 515, check what percentage of your context window you are burning.
2. Cut the dead weight. Remove any server you have not used in the past week. If your stack feels bloated, read how to build a lean, efficient stack.
3. Use the tools. The Token Calculator and Stack Builder on stackmcp.dev 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.