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PromptSpend

Provides sourced, date-verified LLM pricing for estimating usage costs and comparing models.

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Install PromptSpend

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "promptspend": {
      "command": "npx",
      "args": [
        "-y",
        "@promptspend/mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

The install command started, but didn't respond the way we expected when we tried to talk to it.

npx -y @promptspend/mcp

initialize succeeded but no response to tools/list.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 6d ago.

Overview

PromptSpend MCP server connects AI agents to a maintained catalog of LLM pricing with a source and verification date for each rate. Its cost tools cover workload estimates, side-by-side model comparisons, and visible cost-audit receipts. Reach for it when planning an AI feature, checking model economics, or investigating how input, output, history, caching, and reasoning affect spend. Pricing is re-fetched each morning and checked before updated data is accepted.

Use cases

  • Estimate the monthly cost of an AI feature before implementation
  • Compare model pricing for a realistic prompt and usage scale
  • Audit input, output, history, caching, and reasoning-token costs
  • Review provider free-tier rules and model price changes

Key features

  • Source-linked LLM pricing with verification dates
  • Input and output cost estimation
  • Side-by-side comparison for up to four models
  • Cost-audit receipts with major cost drivers
  • Free-tier and provider policy tracking
  • Daily catalog refresh with validation checks

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

21/22 checks healthy over the last 92d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 4d ago via OSV.dev · @promptspend/mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars1Stargazers on the source repository.
npm downloads1.3kPackage downloads in the last 30 days.
Last commitTodayMost recent push to the default branch.
Install checkInconclusiveStarted but did not complete the MCP handshake in our test.

How PromptSpend works

What PromptSpend does

PromptSpend gives AI agents access to an LLM pricing catalog designed for cost analysis rather than a static list of rates. Each pricing entry carries provenance and the date it was last checked, allowing estimates to be reviewed against the vendor source. The catalog covers model prices, provider free-tier information, and the assumptions used in calculations.

The main use is planning an AI feature or workload. A developer can estimate spending for an interaction, compare several models, inspect per-user and margin figures, and create a cost-audit receipt that summarizes the visible workload. Results identify major cost drivers and can suggest lower-cost models to test without treating price as evidence of equivalent quality.

How it works

A scheduled GitHub Action refreshes the catalog every morning from independent sources. PromptSpend combines those sources using an explicit trust order, applies sanity checks, and either commits the refreshed result or opens a pull request for human review. Capture rules operate at the model-family level, so new versions can be included without changing application code for each release.

Calculations separate input and output pricing. They can account for repeated chat history, model-specific tokenization, cache writes, long-context pricing tiers, hidden reasoning tokens, promotional-rate dates, and output ceilings. Assumptions and exclusions are shown with the result instead of being hidden in the calculation.

Free-tier records include provider rules, limits, card requirements, and whether free usage trains models. These facts are quoted from vendor pages with links and dates, then checked during the same morning refresh process.

Tools and capabilities

  • Estimate monthly, yearly, per-user, and margin-related costs for a described interaction.
  • Compare up to four models side by side for a workload.
  • Browse a sortable catalog with provider, source, and verification details.
  • Produce a cost-audit receipt containing workload estimates, leading cost drivers, and candidate cheaper models.
  • Review free-tier conditions across providers.
  • Inspect pricing provenance, pipeline status, and flagged data.

Limitations and notes

Estimates are not a substitute for measuring production traffic. Exact token counting is available for OpenAI-family models using their tokenizer; other model families use a clearly labeled calibrated ratio. Price does not establish response quality, so suggested alternatives require testing.

Caching is disabled by default because cache writes are billed separately. Promotional prices are applied only within their stated date window. Requests that exceed a model's context window or output limit are flagged as infeasible rather than assigned a misleading price.

Price alerts are opt-in and depend on a configured deployment. Available delivery methods include an Atom feed, repository pull requests, browser push, and an email digest. Email subscriptions store the address, followed models, and signup date; browser push uses the browser-issued subscription URL.

Read the full README View source on GitHub

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Frequently Asked Questions about PromptSpend

PromptSpend is an MCP server that connects AI agents to the PromptSpend catalog of LLM prices and supports cost estimation, model comparison, and cost-audit receipts. It uses pricing entries with sources and verification dates, helping developers assess input, output, usage scale, and likely model costs before building.

PromptSpend refreshes its pricing catalog every morning. A GitHub Action retrieves data from independent sources, applies trust and sanity checks, then commits the result or opens a pull request for human review.

Yes, PromptSpend prices input and output separately and accounts for repeated chat history. Its calculations can also include model-specific tokenization, cache writes, long-context pricing tiers, and hidden reasoning tokens.

Yes, PromptSpend compares up to four models for a described interaction. Results can include monthly, yearly, per-user, and margin-related figures, along with the largest cost drivers.

Yes, PromptSpend tracks provider free-tier availability, card requirements, usage limits, and model-training policies. Each fact is linked to and dated against the provider's own page.

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