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Claude Cost Audit

Claude Cost Audit

Remote
github.com/tylerscomic-labDeveloper Tools0Updated 14w ago
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About Claude Cost Audit

Claude Cost Audit is an MCP server for developers checking Claude API costs and tokenization assumptions. It calculates costs with prompt-cache pricing and scans source for use of foreign tokenizers with Claude.

Key features

  1. 01Per-model Claude cost calculation
  2. 02Prompt-cache break-even analysis
  3. 03Cacheable-prefix threshold checks
  4. 04Foreign tokenizer usage scanner

Use cases

  1. 01Estimate API spend from token counts
  2. 02Compare cache savings by TTL
  3. 03Find tiktoken use near Claude API code

MCP support

The MCP server exposes tools for calculating Claude message costs, checking cache break-even points, and auditing tokenizer usage.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://claude-cost-audit-mcp.mcpize.run/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

Details

From the project README

claude-cost-audit-mcp

License: MIT Live on MCPize

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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