Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Artificial Analysis MCP Server List the top 5 models with the highest intelligence index"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP (Model Context Protocol) server that provides LLM model pricing, speed metrics, and benchmark scores from Artificial Analysis.
Get real-time pricing for 300+ LLM models (input/output/blended rates)
Compare speed metrics (tokens/sec, time to first token)
Access benchmark scores (Intelligence Index, Coding Index, MMLU-Pro, GPQA, and more)
Filter by provider (OpenAI, Anthropic, Google, etc.)
Sort by any metric
Related MCP server: LLM Benchmark MCP Server
claude mcp add artificial-analysis -e AA_API_KEY=your-key -- npx -y artificial-analysis-mcpOr install from GitHub:
claude /mcp add https://github.com/davidhariri/artificial-analysis-mcpAdd to your Claude settings (~/.claude/settings.json):
{
"mcpServers": {
"artificial-analysis": {
"command": "npx",
"args": ["-y", "artificial-analysis-mcp"],
"env": {
"AA_API_KEY": "your-api-key"
}
}
}
}Environment Variable | Required | Description |
| Yes | Your Artificial Analysis API key |
Get your API key at artificialanalysis.ai.
list_modelsList all available LLM models with optional filtering and sorting.
Parameters:
Name | Type | Required | Description |
| string | No | Filter by model creator (e.g., "OpenAI", "Anthropic") |
| string | No | Sort field (see below) |
| string | No | "asc" or "desc" (default: "desc") |
| number | No | Maximum results to return |
Sort fields: price_input, price_output, price_blended, speed, ttft, intelligence_index, coding_index, math_index, mmlu_pro, gpqa, release_date
Example usage:
"List the top 5 fastest models"
"Show me Anthropic models sorted by price"
"What are the cheapest models with high intelligence scores?"
get_modelGet detailed information about a specific model.
Parameters:
Name | Type | Required | Description |
| string | Yes | Model name or slug (e.g., "gpt-4o", "claude-4-5-sonnet") |
Returns: Complete model details including pricing, speed metrics, and all benchmark scores.
Example usage:
"Get pricing for GPT-4o"
"What are Claude 4.5 Sonnet's benchmark scores?"
Each model includes:
Pricing: Input/output/blended rates per 1M tokens (USD)
Speed: Output tokens per second, time to first token
Benchmarks: Intelligence Index, Coding Index, Math Index, MMLU-Pro, GPQA, LiveCodeBench, and more
# Install dependencies
npm install
# Build
npm run build
# Run locally
AA_API_KEY=your-key node dist/index.jsMIT
Get detailed information about a specific LLM model including pricing (input/output/blended per 1M tokens), speed metrics (tokens/sec, TTFT), and benchmark scores (Intelligence Index, Coding Index, MMLU-Pro, GPQA, etc.).
Inputs: model (required).
List all available LLM models from Artificial Analysis with pricing, speed, and benchmark data. Filter by creator (OpenAI, Anthropic, Google, etc.) and sort by price, speed, or benchmark scores.
Inputs: creator, sort_by, sort_order, limit.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 2 tools
The two tools have clearly distinct purposes: 'get_model' retrieves detailed information about a specific model, while 'list_models' provides a filtered and sortable overview of all available models. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on whether it needs detailed data on one model or a broader list.
Both tool names follow a consistent verb_noun pattern ('get_model' and 'list_models'), using simple, descriptive verbs that clearly indicate the action. The naming is uniform and predictable, with no deviations in style or convention.
With only two tools, the server feels under-scoped for its apparent domain of LLM model analysis. While the tools cover basic retrieval and listing, a more comprehensive server might include operations like comparing models, updating model data, or managing user preferences, making the current set feel thin and potentially limiting for agent workflows.
The tool set is severely incomplete for a server focused on LLM model analysis. It lacks essential operations such as comparing models, filtering by specific benchmarks, updating or adding model information, and handling user-specific queries or alerts. This creates significant gaps that could lead to agent failures when trying to perform common analytical tasks.
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