MCP server for Artificial Analysis API - get LLM model pricing, speed, and benchmarks
MCP server for Artificial Analysis API - get LLM model pricing, speed, and benchmarks. Exposed over MCP by the artificial analysis mcp mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
An MCP (Model Context Protocol) server that provides LLM model pricing, speed metrics, and benchmark scores from Artificial Analysis.
Everything the assistant can do here goes through one of these:
list_models — List all available LLM models with optional filtering and sortingget_model — The get_model tool exposed by this serverYou will need one environment variable: AA_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Installation goes through your MCP client rather than a global install: point it at artificial-analysis-mcp on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Artificial Analysis MCP's toolset — list_models, get_model — is a fair guide to whether it matches your workflow. It is maintained by davidhariri; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Artificial Analysis MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| list_models | List all available LLM models with optional filtering and sorting. |
| get_model | The get_model tool exposed by this server. |
{
"mcpServers": {
"artificial-analysis": {
"command": "npx",
"args": ["-y", "artificial-analysis-mcp"],
"env": {
"AA_API_KEY": "your-api-key"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| AA_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.