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Glama
davidhariri

Artificial Analysis MCP Server

by davidhariri

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
AA_API_KEYYesYour 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

CapabilityDetails
tools
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
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

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A3.6/5.0

Scored across 2 tools

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness2/5

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.

Maintenance

ActivityInactive
ResponsivenessNo issues