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Glama
WaveSpeedAI

WaveSpeed MCP Server

Official

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
WAVESPEED_API_KEYYesWaveSpeed API key from https://wavespeed.ai/accesskey. Optional if the WaveSpeed CLI is already logged in.
WAVESPEED_BASE_URLNoOverride the WaveSpeed API base URL.https://api.wavespeed.ai
WAVESPEED_CLIENT_NAMENoClient name reported for usage attribution.wavespeed-mcp

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_modelsA

Search the live WaveSpeed model catalog (image, video, audio, 3D). Returns model IDs usable with run_model. Do not invent model IDs — always pick one returned by this tool.

get_model_schemaA

Get a model's real input schema (required fields, properties, defaults). Call this before run_model so inputs match what the model actually accepts.

run_modelA

Run any WaveSpeed model. input keys come from get_model_schema. Local files: pass "@./path" string values — they are uploaded and replaced with hosted URLs (bare paths are NOT uploaded). Returns output URLs. If the wait limit is hit, the task keeps running; recover it with get_prediction.

get_priceA

Estimate the cost of a run before executing it (no charge). Provide the same input you would pass to run_model — pricing often depends on inputs like duration or resolution.

get_balanceA

Show the WaveSpeed account credit balance.

upload_fileA

Upload a local file to WaveSpeed and get its hosted URL (identical bytes reuse the same upload for 24h). Usually unnecessary — run_model handles "@./path" inputs itself.

get_predictionA

Fetch the status and outputs of a past or in-flight prediction by id — use to recover a run that hit the wait limit.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A4.4/5.0

Scored across 7 tools

Disambiguation4/5

Each tool has a clear purpose: model discovery, schema, execution, pricing, balance, upload, and status. There is slight overlap between run_model and upload_file since run_model can handle uploads, but descriptions clarify when upload_file is needed.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with clear verbs (list, get, run, get, get, upload, get). No mixed conventions or vague naming.

Tool Count5/5

With 7 tools, the server covers the essential workflow for model execution and account management without unnecessary bloat. Each tool serves a distinct need in the lifecycle.

Completeness4/5

The tool set covers model discovery, schema retrieval, execution, pricing, balance, file upload, and result retrieval. A minor gap is the absence of a cancel/delete prediction tool, but the core workflow is well covered.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive