Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
LLM pricing where every rate carries its source and the date it was last confirmed.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Funktioniert in Claude im Web, auf dem Desktop und auf dem Handy:
https://promptspend.dev/mcpÖffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"promptspend": {
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add --transport http promptspend https://promptspend.dev/mcpclaude mcp add promptspend -- npx -y @promptspend/mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"promptspend": {
"url": "https://promptspend.dev/mcp"
}
}
}In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"promptspend": {
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"promptspend": {
"type": "http",
"url": "https://promptspend.dev/mcp"
}
}
}In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"promptspend": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@promptspend/mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
ChatGPT verbindet sich nur mit Servern im Internet:
https://promptspend.dev/mcpMenünamen können sich je nach Tarif und mit der Zeit leicht unterscheiden.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Tool-Namen und -Beschreibungen genau so, wie der Server sie am 7. Nov. 2026 gemeldet hat. Geschrieben hat sie der Entwickler, nicht wir; wir zeigen sie als reinen 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.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto AndrewAvery7 bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Antwort in 69 ms (HTTP 200)
Keine Anmeldung nötig
Wir haben 3 Tool-Beschreibungen gelesen und keine versteckten Anweisungen an die KI gefunden
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 3 von 3 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Beides geht: Installiere ihn auf deinem Computer oder verbinde dich mit der Cloud-Version.
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.1.11) kam vor 8 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Er verbindet sich per URL, also funktionieren auch Apps, die Remote-MCP-Server akzeptieren, zum Beispiel ChatGPT im Entwicklermodus.
In derselben Kategorie: Context7, Codebase Memory, Exa
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Up-to-date docs for AI agents
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.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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.