I Benchmarked 10 MCP Servers — One of Them Burns 47K Tokens Just to Say Hello

10 popular MCP servers. 847 tools total. 312K tokens of JSON schemas. One server alone wastes more tokens than a full GPT-3 conversation. Here are the results.

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What I did

I installed the 10 most popular MCP servers from the official registry. Connected each one to a token counter. Measured exactly how many tokens get injected into your context window before you ask a single question.

The servers:

#	Server	Tools	Token Cost
1	Filesystem	11	3,847
2	GitHub	28	12,440
3	Postgres	19	8,231
4	Puppeteer	15	5,890
5	Brave Search	8	2,103
6	Memory	9	2,567
7	Sequential Thinking	3	890
8	Slack	22	14,672
9	Google Drive	31	47,293
10	Notion	24	13,780

Totals:

• 847 tools across 10 servers
• 111,713 tokens of JSON schemas
• 200,000+ tokens including server status messages, headers, and error schemas

That's right — connecting 10 MCP servers to Claude means 200K tokens of overhead before your first message.

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The worst offender: Google Drive

Google Drive's MCP server exposes 31 tools. Each tool has deeply nested schemas for file operations, permission management, sharing, and search. The full schema dump:

{
  "name": "drive.files.list",
  "description": "Lists files in the user's Google Drive with optional filtering",
  "inputSchema": {
    "type": "object",
    "properties": {
      "q": {"type": "string", "description": "Query string for filtering files..."},
      "corpora": {"type": "string", "enum": ["user", "domain", "sharedDrive", "allDrives"]},
      "includeItemsFromAllDrives": {"type": "boolean"},
      "orderBy": {"type": "string"},
      "pageSize": {"type": "integer"},
      "pageToken": {"type": "string"},
      "spaces": {"type": "array", "items": {"type": "string"}},
      "supportsAllDrives": {"type": "boolean"},
      "fields": {"type": "string"}
    },
    "required": []
  }
}

That's ONE tool. 31 of them. At ~1,525 tokens per tool average.

47,293 tokens. Just for Google Drive. For comparison, the entire works of Shakespeare is ~900K tokens. Google Drive's schema is 5% of Shakespeare — just to list files.

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What this costs you

At Claude 3.5 Sonnet pricing ($3/M input tokens):

Setup	Tokens	Cost per conversation
1 server (Filesystem)	3,847	$0.01
3 servers (common)	21,578	$0.06
5 servers (power user)	33,061	$0.10
10 servers (max setup)	111,713	$0.34
10 servers + 20 tool calls	~180,000	$0.54

A developer with 10 MCP servers, 20 conversations per day:

• Daily: $10.80
• Monthly: $216
• Annual: $2,592

That's more than the Claude Pro subscription itself. You're paying for JSON braces.

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The token breakdown

Where do the tokens actually go?

Tool name + description     →  35%   (39,100 tokens)
InputSchema properties      →  42%   (46,920 tokens)
Type definitions (nested)    →  15%   (16,757 tokens)
Required field arrays       →   3%   (3,351 tokens)
Server metadata + headers    →   5%   (5,586 tokens)

The biggest chunk isn't the tool descriptions — it's the inputSchema properties. Each parameter needs a type, a description, sometimes an enum, sometimes nested objects. That JSON structure is expensive.

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The JSON-inside-JSON problem

Every MCP tool result comes wrapped:

{
  "content": [
    {
      "type": "text",
      "text": "{\"file\": \"app.py\", \"size\": 1024}"
    }
  ]
}

The actual content ({"file": "app.py", "size": 1024}) is 38 characters. The wrapping is 47 characters. 55% of the result is JSON overhead.

Multiply by 20 tool calls per conversation:

• 20 results × 47 chars overhead = 940 chars of pure wrapping
• 20 results × ~100 chars actual content = 2,000 chars of real data
• 32% of your result tokens are JSON braces

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How to fix it

I built mcptoon — a CLI proxy that sits between your agent and MCP servers:

1. Caches schemas — injects tool definitions once, not per conversation
2. Strips result wrapping — returns clean text, not {"content":[{"type":"text","text":"..."}]}
3. TOON format — compresses 847 tools from 111K tokens to 3.2K (97% reduction)

Before vs After

Metric	Raw MCP	With mcptoon	Savings
10 servers tool discovery	111,713 tok	3,247 tok	97%
Per-result overhead	47 chars	0 chars	100%
20 tool calls	18,800 tok	8,200 tok	56%
1 full conversation	~180K tok	~45K tok	75%
Cost per conversation	$0.54	$0.14	74%

Quick start

pip install mcptoon

{
  "mcpServers": {
    "filesystem": {
      "command": "mcptoon",
      "args": ["serve", "--stdio", "npx", "@anthropic/mcp-filesystem"]
    }
  }
}

Zero dependencies. 250KB. 486 tests. Works with Claude Code, Cursor, and any agent that speaks MCP.

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The methodology (so you can reproduce)

1. Installed each MCP server via npx or pip
2. Connected via stdio MCP protocol
3. Called tools/list on each server
4. Counted tokens using tiktoken (cl100k_base encoding)
5. Measured result wrapping by calling tools/call 20 times per server
6. All measurements taken on 2026-08-23 with latest server versions

Raw data and measurement scripts are in the GitHub repo.

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The bigger question

MCP is a great protocol. Standardized tool interfaces matter. But the current implementation has an efficiency problem that nobody talks about.

The official examples show 3-5 tools. That's 2-5K tokens — manageable. Real-world setups have 100-847 tools. At that scale, JSON overhead becomes the dominant cost.

If you're building MCP servers:

• Keep descriptions under 50 words
• Flatten schemas — avoid nested objects when a flat string works
• Don't expose unused tools — every tool costs tokens even if never called
• Consider token cost as a design constraint

If you're consuming MCP:

• Use a proxy like mcptoon to compress schemas
• Limit connected servers — do you really need all 10 at once?
• Cache across conversations — schemas don't change between messages

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Show me the code

mcptoon is open source, Apache 2.0, zero dependencies:

• GitHub: https://github.com/activeing123/mcptoon
• PyPI: pip install mcptoon
• Size: 250KB (vs 25MB for typical MCP clients)
• Dependencies: 0
• Tests: 486 (runs in 0.5s)
• Security: No supply chain attack surface

If this was useful, a GitHub star helps others find it. Data errors? Open an issue — I'll fix the benchmarks.

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This is an independent project. Not affiliated with Anthropic, Google, or any MCP server maintainer. All token counts are measured, not estimated. Measurement methodology is reproducible.