Artificial Analysis MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| AA_API_KEY | Yes | Your Artificial Analysis API key |
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
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_modelsA | 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. |
| get_modelA | 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.). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
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.