@promptspend/mcp

LLM pricing for coding agents — every price carries its source and the date it was last confirmed.

There are already MCP servers that will tell you what a model costs. This one tells you where the number came from, when it was last checked, and which numbers two sources disagree about. And it will cost a described workload rather than just quoting a rate, because the arithmetic between "$3 per million tokens" and "$4,955 a month" is where nearly every estimate goes wrong.

claude mcp add promptspend -- npx -y @promptspend/mcp

No API key. No account. MIT.

Why another pricing server

Every price comes with its paperwork.

// Real output, not an illustration: `get_price` for gpt-5 on 2026-08-03.
{
  "model": "gpt-5",
  "input_per_million_usd": 1.25,
  "output_per_million_usd": 10,
  "provenance": {
    "source": "vendor",
    "source_description": "Hand-verified against the provider's own published pricing page.",
    "last_verified": "2026-08-03",
    "disputed": false,
    "upstream_stale": false,
    "confidence": "Confirmed against the provider's own pricing page on 2026-08-03.",
    "verified_url": "https://developers.openai.com/api/docs/pricing",
  },
}

last_verified moves as rows are re-read, so the date here will age; the shape will not. What is worth checking is that the block is present at all — a price without one is a price you cannot audit.

That matters more to an agent than to a person. Someone reading a web page sees the interface around the number and forms their own view of how much to trust it. A model handed a bare figure has nothing, and will repeat it with whatever confidence the phrasing implies. Given the date and the source it can say "as of 1 August, per OpenAI's pricing page" instead of stating a number as though it were timeless.

And it says when it does not know. Where two sources disagree and no human has adjudicated, the response is marked disputed: true with both figures, rather than quietly picking one:

DISPUTED — two sources disagree on this price and no human has adjudicated it.
The figure shown is the primary feed's number, last confirmed 2026-08-02.
Treat it as indicative and say so.

The tool a price lookup cannot provide

estimate_cost runs promptspend.com's own cost engine — imported, not reimplemented, so a number this server reports and a number the website shows cannot drift apart. A test asserts it.

It accounts for the things hand-rolled estimates miss:

• Conversation history compounds. Turn N re-sends turns 1…N−1 as input, so cost grows with the square of the turn count.
• Cache writes cost more than input — 1.25× at both OpenAI and Anthropic. Counting only the cheaper reads reports a saving your invoice will not have.
• Long-context tiers apply per request, not per conversation.
• Reasoning tokens are billable even though you never see them.
• Output is priced separately, typically 3–5× input.

"What would a support assistant cost at 4,000 conversations a day on GPT-5 versus DeepSeek V3.2?"

Context footprint

MCP tool definitions are loaded into your context on every turn, for as long as the server is connected. A heavy server can add thousands of tokens to every message, which is why the advice going round is to prefer a CLI for read-only data.

A pricing server that quietly taxes every turn, to tell you about token costs, would be an easy and deserved joke. So:

This server adds ~700 tokens per turn. Three tools, short descriptions, budgeted at 900 and checked in CI by npm run check:footprint.

If that number ever stops being true, the build fails — the README figure is asserted against the actual manifest, not typed once and trusted.

Tools

| Tool | What it answers | | --------------- | ------------------------------------------------------------ | | get_price | What does this model cost, and how much should I trust that? | | estimate_cost | What will my workload actually cost per month on each? | | find_cheaper | What could I test that is cheaper and not obviously worse? |

find_cheaper returns candidates to test, never a recommendation — the capability index behind it is an illustrative estimate, not a benchmark, and the response says so.

Install

Claude Code

claude mcp add promptspend -- npx -y @promptspend/mcp

Cursor / Claude Desktop / Windsurf — in mcp.json:

{ "mcpServers": { "promptspend": { "command": "npx", "args": ["-y", "@promptspend/mcp"] } } }

What it will not do

• It will not serve you a stale price. The catalog is fetched, never bundled, because a bundled one is only as current as the last publish. If it cannot be reached, the tools return an error rather than an old number — in an agent context a stale figure gets relayed with full confidence, which is the exact failure this project exists to prevent.
• It has no benchmarks, latency or endpoint data. Other servers do. Three tools is a deliberate choice about your context budget.
• It does not track you. No key, no account, no logging of who calls it — the same promise as the API it reads.

Scope of the prices

Standard-tier, global-endpoint list prices in USD. Not modelled: regional and data-residency premiums, priority tiers, server-side tool-call fees, fine-tuning, and negotiated or committed-use discounts.

Part of PromptSpend — MIT. Found a wrong price? That is the most serious class of bug this project can have. Open an issue.