Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
LLM pricing where every rate carries its source and the date it was last confirmed.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
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Works in Claude on the web, desktop and mobile:
https://promptspend.dev/mcpOpen Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"promptspend": {
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}Run this in your terminal
claude mcp add --transport http promptspend https://promptspend.dev/mcpclaude mcp add promptspend -- npx -y @promptspend/mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"promptspend": {
"url": "https://promptspend.dev/mcp"
}
}
}Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"promptspend": {
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"promptspend": {
"type": "http",
"url": "https://promptspend.dev/mcp"
}
}
}Paste into .vscode/mcp.json in your project
{
"servers": {
"promptspend": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
ChatGPT connects only to servers on the internet:
https://promptspend.dev/mcpMenu names can differ slightly by plan and over time.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Tool names and descriptions exactly as the server reported them on Nov 7, 2026. The maker wrote them, not us; we show them as plain text.
get_priceList price for one LLM in USD per million tokens, with its source and last-confirmed date. Returns disputed=true when two sources disagree, and close matches when the model is unknown. To cost a workload, use estimate_cost.
estimate_costMonthly cost of a described workload, or of measured usage, on each model. Accounts for history compounding, cache writes, long-context tiers and reasoning tokens. Use this instead of multiplying prices by hand.
find_cheaperCheaper models that score at or above the given one on capability, costed on the same workload. Returns candidates to test, not a recommendation.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account AndrewAvery7, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
Responded in 69 ms (HTTP 200)
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Read 3 tool descriptions and found no hidden instructions to the AI
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 3 of 3 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
Either way works: install it on your computer or connect to the cloud version.
The GitHub repo last changed –, and the latest version (0.1.11) reached the registry 9 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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.
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.
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:
_"What would a support assistant cost at 4,000 conversations a day on GPT-5
versus DeepSeek V3.2?"_
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 ~897 tokens per turn. Three tools, short descriptions,
budgeted at 1,200 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.
| Tool | What it answers |
|---|---|
get_price | What does this model cost, and how much should I trust that? With as_of, what did it cost then? |
estimate_cost | What will _my_ workload cost per month on each? With usage, what would these totals have cost? |
find_cheaper | What could I test that is cheaper and not obviously worse? |
`get_price` with `as_of: "2026-09-15"` returns the rate PromptSpend published on that UTC day, read from the price history — useful for checking an old invoice. A day before PromptSpend began tracking the model is answered "not tracked", never with the first rate seen, and a later correction (PromptSpend had been reading the wrong page) is returned with the figure, so an agent does not present a misreading as what the vendor charged.
`estimate_cost` with `usage` prices measured totals — say a month from a usage export — instead of a described workload: { "input": 1200000, "output": 300000, "cache_read": 4000000, "cache_write": 200000 }, where input excludes cache reads. A model without a published cache rate is billed at its full input rate (and the response says so), and a model with a long-context tier is flagged, because totals cannot show which requests crossed it.
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.
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"] } } }
No install — hosted at https://promptspend.dev/mcp. Same tools, same answers, imported from this package rather than copied. In Claude.ai or ChatGPT, add it as a custom connector with that URL; in Claude Code:
claude mcp add --transport http promptspend https://promptspend.dev/mcp
The hosted server receives your tool arguments (a model name, a workload's token counts) to answer them, and stores none of them; it needs no key or account. Like the rest of the pricing API at promptspend.dev, it has Cloudflare's request logging switched on: each entry records the time, the address (/mcp), request headers and connection details such as an IP address, kept for up to seven days. PromptSpend's code never logs the arguments themselves. Run the npm package instead if a workload description should never leave your machine.
Or as an Agent Skill, which costs context only when a price question comes up rather than on every turn: see skills/.
catalog_status warning written for the agent to relay. Past that bounded grace period, the tools return an error rather than an old number.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.