mcpField
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PromptSpend

PromptSpend

Remote
promptspend.comAI & Agents1Updated today

About PromptSpend

PromptSpend provides sourced, dated LLM pricing and estimates the cost of a described workload. It is for coding agents and developers comparing model costs, and marks disputed prices rather than silently choosing between sources.

Key features

  1. 01Returns model prices with source and verification date
  2. 02Flags prices disputed by sources
  3. 03Estimates monthly workload costs
  4. 04Finds cheaper candidates to test
  5. 05Warns about stale catalog data

Use cases

  1. 01Auditing the source and age of a model price
  2. 02Estimating monthly cost for an assistant workload
  3. 03Finding lower-cost models to evaluate

MCP support

The MCP server exposes get_price, estimate_cost, and find_cheaper tools to MCP clients.

Configuration

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

Endpointhttps://promptspend.dev/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.

Packages
npm@promptspend/mcp0.1.11

Tools

3
ParameterTypeRequiredDescription
model_namestringYesModel id or name, e.g. gpt-5 or "Claude Opus 5".

Details

From the project README

@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

ToolWhat it answers
get_priceWhat does this model cost, and how much should I trust that?
estimate_costWhat will my workload actually cost per month on each?
find_cheaperWhat 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 pass off a stale price as current. The catalog is fetched, never bundled. A retained copy may be used for at most 24 hours when the live file cannot be reached, and every affected tool response carries an explicit catalog_status warning written for the agent to relay. Past that bounded grace period, the tools return an error rather than an old number.
  • It has no benchmarks, latency or endpoint data. Other servers do. Three tools is a deliberate choice about your context budget.
  • It has no telemetry. The server itself reports nothing about you, and needs no key and no account — the same 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.


The same catalog is also in free native apps for iPhone and iPad and Android.

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

Comments

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