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Estimate LLM token spend for coding tasks with ranges, scenarios, and confidence.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "Budgetary: estimate token spend" MCP server from askskill: Run: claude mcp add 'io-github-thriftell-budgetary' -- npx -y @budgetary/mcp
Estimate token spend for this coding task: split a 50k-line Node.js monolith into 3 services and add basic tests. Provide low/typical/high ranges, key assumptions, confidence, and the main cost drivers.
An estimate with token ranges, scenario assumptions, confidence, and major cost drivers.
Compare token costs for two tasks: 1) add documentation and type hints to an existing Python project; 2) rewrite a core module and add integration tests. Give estimated ranges, confidence, and say which is more token-efficient.
A side-by-side cost comparison with a conclusion on which option uses fewer tokens and why.
We plan to use AI for a medium-sized frontend feature. Do a pre-flight token budget estimate across four phases: requirements clarification, code generation, debugging, and test completion, then recommend a total budget.
A phase-by-phase token budget estimate and an actionable total budget recommendation.
Lets users choose response depth and token usage before answering.
Forecast campaign budget scenarios and compare projected spending options quickly.
Count tokens and estimate costs across many LLMs for planning and budgeting.
Search live LLM pricing, compare models, and estimate usage costs.
Query Claude Code usage and costs with natural-language spend analysis insights.
Discover live models and pricing to route tasks to compatible low-cost LLMs.