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estimate_cost

Estimate monthly cost for LLM usage by providing input and output token counts, model or hardware, and customizing parameters like caching, batch mode, and electricity rate.

Instructions

Estimate monthly cost for LLM usage — API providers OR self-hosted hardware.

Args: input_tokens_per_month: total input tokens per month output_tokens_per_month: total output tokens per month model_or_hardware: specific model/hw ID (e.g. 'gpt-5', 'rtx-5090'). If None, returns full comparison table. amort_months: amortization period for hardware purchase, default 24 months electricity_eur_per_kwh: electricity rate, default 0.20 EUR/kWh (FR 2026 average) hours_active_per_day: hours of active inference per day, default 4 caching_hit_rate: 0..0.9 fraction of input tokens hitting prompt cache (Anthropic 90% off / Gemini 75% off). Default 0. batch_mode: True applies 50% off input+output for batchable APIs (24h delay). Default False. off_peak: True applies DeepSeek off-peak discount (50% V3.5 / 75% R1). Default False. pc_always_on: True = idle (24-hours_active) × idleW. False = PC off out of active hours (0W idle). Default True. vat_recoverable: True = subtract 16.67% VAT from hardware purchase price (HT pour pro). Default False.

Returns: cost estimate in EUR (with USD-to-EUR conversion ~0.92 for APIs)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
off_peakNo
batch_modeNo
amort_monthsNo
pc_always_onNo
vat_recoverableNo
caching_hit_rateNo
model_or_hardwareNo
hours_active_per_dayNo
input_tokens_per_monthYes
electricity_eur_per_kwhNo
output_tokens_per_monthYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and excels. It discloses all behavioral traits: parameter effects (caching discounts, batch/off-peak discounts, VAT handling), default values, and return format (EUR cost with USD conversion). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose sentence followed by detailed bullet points. It is somewhat lengthy but efficient for the complexity. Every sentence provides value; no waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 11 parameters, no output schema, and high complexity, the description is complete. It covers all inputs with defaults, return format, and conversion notes. No gaps identified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description explains all 11 parameters in detail with examples and default behaviors (e.g., 'caching_hit_rate: 0..0.9 fraction... Default 0.'). This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it estimates monthly cost for LLM usage across API providers and self-hosted hardware, using specific verbs and resources. It distinguishes from sibling tools like compare, estimate_vram, get_model, list_models, search_models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: if model_or_hardware is None, returns full comparison table; billing options are detailed. However, it does not explicitly state when to use this tool versus siblings like compare or estimate_vram, lacking explicit when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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