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Exact Claude API cost calc with real cache economics, plus a tiktoken-misuse scanner.
About
Exact Claude API cost calc with real cache economics, plus a tiktoken-misuse scanner.
Remote endpoints: streamable-http: https://claude-cost-audit-mcp.mcpize.run/mcp
Security Report
Valid MCP server (3 strong, 3 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
Endpoint verified · Requires authentication · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Found in Source Code
Found by scanning the linked source code. This listing connects to a hosted endpoint, so none of this runs on your machine: it describes what the server software does where it is hosted.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-tylerscomic-lab-claude-cost-audit-mcp": {
"url": "https://claude-cost-audit-mcp.mcpize.run/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
claude-cost-audit-mcp
An MCP server for exact Claude API cost calculation — correct prompt-cache economics (write 1.25x at 5-minute
TTL / 2x at 1-hour TTL, read 0.1x), cache break-even analysis, and a scanner for the single most common
Claude-cost mistake: estimating tokens with OpenAI's tiktoken.
The mistake this catches
Per Anthropic's own documentation: "Do not use tiktoken. It's OpenAI's tokenizer. It undercounts Claude tokens by
~15-20% on typical text, and by much more on code or non-English input." Any cost estimate or context-budget
check built on tiktoken for a Claude model is silently wrong — this scans source for that exact pattern
(foreign tokenizer + Claude/Anthropic usage in the same file) and points to the fix
(messages.count_tokens).
Cache economics people get wrong
Caching doesn't pay off starting from the second request the way most people assume. The real break-even is
2 reuses at 5-minute TTL, but 3 reuses at 1-hour TTL — because the 1-hour write costs more (2x vs 1.25x).
check_cache_breakeven computes this from Anthropic's real multipliers instead of a guessed rule of thumb, and
flags prefixes below the model's own minimum-cacheable-token threshold (which is not monotonic across model
generations — 512 tokens on the newest models, 4096 on some older ones).
Tools
calculate_message_cost
Exact $ cost from token counts, using Anthropic's real current per-model pricing (including Claude Sonnet 5's time-limited introductory rate, resolved automatically from the date you pass).
check_cache_breakeven
Given expected request volume reusing the same prefix, tells you whether caching saves money and at which TTL.
audit_tokenizer_usage
Scans source for tiktoken/gpt-tokenizer/etc. alongside Claude API usage.
Use it
Hosted (recommended): MCPize — free tier, $7/mo Pro.
Self-host:
npm install
node server.js
Part of a small suite
mcp-schema-audit-mcp, cron-schedule-audit-mcp, regex-safety-audit-mcp.
License
MIT
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