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Overview
From the project README
WaveSpeed MCP Server
An MCP server for the WaveSpeed platform: run any model on the live catalog — image, video, audio, 3D — from Claude Code, Claude Desktop, Cursor, Cline, or any MCP client.
Built on the same design as the open-source @wavespeed/cli:
No hardcoded models. The catalog comes from the live API (1h cache); new platform models work the day they ship.
One generation verb.run_model executes anything; get_model_schema tells the agent what inputs a model actually accepts, so it reads before it writes.
Inputs are never mutated. The one explicit transform is the @path marker — a "@./photo.jpg" value inside input uploads the file and substitutes its hosted URL. Bare paths are passed through untouched.
Quote before you spend.get_price takes the same input as run_model and returns an estimate; the amount actually charged for a run is authoritative.
Tools
Tool
What it does
list_models
Search the live catalog by text or modality type
get_model_schema
A model's real input schema (required, properties, defaults), with a link to the model's playground page
run_model
Submit + wait; rejects input keys the model doesn't accept; @path inputs upload automatically; returns output URLs
get_price
Cost estimate for the inputs you pass (no charge), with a link to the model's playground page
get_balance
Account credit balance, with the top-up link
list_predictions
Run history (default last 24h); filter by model, status, time range
list_billings
Itemized charges and refunds (needs a Billing-access key)
get_usage
Spend, request counts and per-model breakdown (default last 7 days; needs a Billing-access key)
delete_predictions
Delete finished runs from the history (up to 100; needs confirm: true, cannot be undone from here)
upload_file
Local file → hosted URL (24h content-hash dedupe)
get_prediction
Status/outputs of any run by id; optional wait_seconds to wait for it
Some generations take minutes to hours. run_model submits the run and returns the prediction id right away (done: false); the assistant then calls get_prediction with that id and wait_seconds (max 1800) until done is true. The task keeps running server-side, so a dropped connection loses nothing. Pass wait_seconds to run_model to wait for a quick model in the same call. While waiting, both tools send MCP progress notifications that carry the prediction id.
Setup
Remote (nothing to install)
The same server is hosted at:
text
https://api.wavespeed.ai/mcp
Claude on the web / ChatGPT: add a custom connector with that address and approve the connection on the WaveSpeed page that opens. Approving creates a dedicated API key (named MCP · <app>) that you can delete on the API Keys page to disconnect.
Claude Code:claude mcp add --transport http wavespeed https://api.wavespeed.ai/mcp
Cursor:
VS Code:code --add-mcp '{"name":"wavespeed","type":"http","url":"https://api.wavespeed.ai/mcp"}'
Any other client that sends request headers: point it at the URL with Authorization: Bearer <your API key>.
The hosted server has the same tools, with one difference for files: it cannot read files on your computer, so pass public URLs, use create_upload from a tool that can send HTTP requests (it returns a temporary upload address and the download_url to use as an input), or, in ChatGPT, attach the file to the chat (the upload_attachment tool, which only ChatGPT can fill, imports it and returns a download_url). Full guide: wavespeed.ai/docs/mcp.
Local (npx)
@latest makes npx fetch the newest release on each start; without it, npx runs a globally installed @wavespeed/mcp if one exists, however old.
Auth resolves from WAVESPEED_API_KEY, or from the CLI's stored login (wavespeed login) — one login covers both tools. Keys: wavespeed.ai/accesskey.
Claude Code
Terminal
claude mcp add wavespeed -- npx -y @wavespeed/mcp@latest
Claude Desktop / other clients (mcpServers config):
What MCP Server lets your agent do. Expand a tool for its parameters.
Verified live~1.6k tokens of context
Security & health
What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.
ActiveLast checked 2d ago
Recent health check succeeded.
10/10 checks healthy over the last 56d
What it can do
From the 11 tools this server returned when we ran it; 11 declare their own safety hints.
Can delete or modify data · 1Writes files · 1Handles credentials · 2Makes network requests · 10Read-only tools · 9
Network at startup3 connections
Files written at startupNone
Install scriptsNone
npm provenanceSigned build
Package age63 days
Maintainers2
Observed 2026-10-21 in an isolated sandbox. A description of reach, not a security audit.
Supply-chain signal
No high-severity advisories surfaced by our automated scan.
Critical 0High 0Medium 0Low 0
Scanned 1d ago via OSV.dev · @wavespeed/mcp (npm)
Adoption & maintenance
Factual signals from GitHub, npm, and our automated checks — not a rating.
GitHub stars34Stargazers on the source repository.
npm downloads1.5kPackage downloads in the last 30 days.
Last commit2d agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed11Callable tools this server registers over MCP.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.
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Frequently Asked Questions about MCP Server
WaveSpeedAI/mcp-server is an MCP server that connects MCP clients to WaveSpeed’s live catalog of image, video, audio, and 3D models. It provides tools to discover models, inspect schemas, estimate prices, submit generations, check balances, upload files, and retrieve prediction results. Authentication uses a WaveSpeed API key or stored CLI login.
Install it with `claude mcp add wavespeed -- npx -y @wavespeed/mcp` in Claude Code. For Claude Desktop and other clients, configure an MCP server that runs `npx -y @wavespeed/mcp` and provide `WAVESPEED_API_KEY` in its environment.
The server provides `list_models`, `get_model_schema`, `run_model`, `get_price`, `get_balance`, `upload_file`, and `get_prediction`. These tools search the live catalog, inspect model inputs, run generations, estimate costs, check credits, upload files, and retrieve prediction status or outputs.
Pass a local file as an `@path` value inside the model’s `input`, such as `"@./photo.jpg"`. The server uploads the file and substitutes its hosted URL; bare paths are passed through unchanged, and inputs are otherwise not mutated.
If a generation exceeds `run_model`’s `wait_seconds`, the tool returns `done: false` with the prediction ID while the task continues server-side. Call `get_prediction` with that ID to retrieve status and outputs; both tools send progress notifications containing the prediction ID.
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