aa-mcp 0.1.2

MCP server wrapping the Artificial Analysis API for LLM and multimodal model data queries

aa-mcp

MCP server wrapping the Artificial Analysis public API. Enables AI agents to query LLM and multimodal model benchmarks, pricing, speed data, and track model updates via structured diffs.

The PyPI package is aa-mcp; it installs the aa-mcp console command.

Requirements

• Python 3.10+
• uv (for installation and running)
• An Artificial Analysis API key (get one free)

Installation & Running

Use uvx as the standard runtime path:

export ARTIFICIAL_ANALYSIS_API_KEY="aa_your_key_here"
uvx aa-mcp

Environment Variables

Variable	Required	Default	Description
ARTIFICIAL_ANALYSIS_API_KEY	Yes	-	Your AA API key
AA_MCP_SNAPSHOT_DIR	No	~/.local/share/aa-mcp/snapshots/	Directory for update snapshots
AA_MCP_LOG_LEVEL	No	INFO	Log level (DEBUG, INFO, WARNING, ERROR)

Official API Coverage

This server wraps the current free Artificial Analysis API endpoints documented at https://artificialanalysis.ai/api-reference:

Artificial Analysis endpoint	MCP tool
GET /api/v2/data/llms/models	aa_list_llms, aa_get_model, aa_compare_models, aa_list_recent_updates, aa_healthcheck
GET /api/v2/data/media/text-to-image	aa_list_media_models(modality="text-to-image")
GET /api/v2/data/media/image-editing	aa_list_media_models(modality="image-editing")
GET /api/v2/data/media/text-to-speech	aa_list_media_models(modality="text-to-speech")
GET /api/v2/data/media/text-to-video	aa_list_media_models(modality="text-to-video")
GET /api/v2/data/media/image-to-video	aa_list_media_models(modality="image-to-video")
POST /api/v2/critpt/evaluate	aa_evaluate_critpt

MCP Tools

aa_list_llms

List LLM models with filtering and sorting.

• Filters: creator, name, slug (substring match)
• Sort by: intelligence (default), price, speed, ttft, coding, math
• limit: Max results (default 20)

aa_get_model

Get full details for a single model by id, slug, or name.

• Returns candidates if multiple matches found
• Supports partial/fuzzy matching

aa_compare_models

Side-by-side comparison of 2+ models.

• Compares: intelligence, coding, math, pricing, speed, latency
• Returns rankings across all metrics
• Input: list of identifiers (ids, slugs, or names)

aa_list_recent_updates

Detect changes since the last local snapshot.

• New models: present in current data but not in snapshot
• Removed models: present in snapshot but gone from current data
• Changed models: field-level diffs for pricing, speed, intelligence scores, etc.
• First run creates a baseline snapshot
• Float changes below 0.01 threshold are ignored (noise filtering)

aa_list_media_models

Query multimodal / media model rankings.

• Modalities: text-to-image, image-editing, text-to-speech, text-to-video, image-to-video
• top_n: Limit results (default 10)
• include_categories: Per-category Elo breakdown where the upstream endpoint supports it

aa_evaluate_critpt

Submit a complete CritPt benchmark batch to the official evaluation endpoint.

• Requires submissions for the full public CritPt problem set
• Validates required fields before sending: problem_id, generated_code, model, generation_config
• Optional batch_metadata object is passed through to Artificial Analysis
• The upstream endpoint is rate-limited separately and may take substantial time to complete

aa_healthcheck

Verify API key and upstream connectivity.

• Returns masked key preview, model count, rate limit info
• Reports specific error types (auth, rate limit, server error)

Snapshot / Update Tracking

The aa_list_recent_updates tool uses a local JSON snapshot mechanism:

1. First call: Fetches all LLM models, saves a normalized snapshot to disk, reports "baseline created"
2. Subsequent calls: Fetches fresh data, diffs against the latest snapshot, reports changes
3. Snapshot location: ~/.local/share/aa-mcp/snapshots/llm_models_YYYYMMDDTHHMMSSZ.json
4. Noise filtering: Float fields use a 0.01 threshold to avoid reporting insignificant fluctuations
5. Tracked fields: name, slug, creator, all evaluation scores, all pricing fields, speed/latency

opencode Integration

Add to your opencode.json:

{
  "mcp": {
    "servers": {
      "artificial-analysis": {
        "command": "uvx",
        "args": ["aa-mcp"],
        "env": {
          "ARTIFICIAL_ANALYSIS_API_KEY": "aa_your_key_here"
        }
      }
    }
  }
}

For MCP client examples, see docs/mcp-client-config.md.

Example Usage (via MCP client)

# List top 5 most intelligent LLMs
aa_list_llms(sort_by="intelligence", limit=5)

# Get details on Claude 3.5 Sonnet
aa_get_model("claude-3-5-sonnet")

# Compare GPT-4o vs Claude 3.5 Sonnet vs Gemini 1.5 Pro
aa_compare_models(["gpt-4o", "claude-3-5-sonnet", "gemini-1.5-pro"])

# Check for recent model changes
aa_list_recent_updates()

# Top 5 text-to-image models
aa_list_media_models(modality="text-to-image", top_n=5)

# Submit CritPt benchmark results
aa_evaluate_critpt(
  submissions=[
    {
      "problem_id": "Challenge_1_main",
      "generated_code": "def solution(): return 42",
      "model": "example-model",
      "generation_config": {"temperature": 0}
    }
  ],
  batch_metadata={"run_id": "local-test"}
)

# Verify API connectivity
aa_healthcheck()

Development Checks

For development, run the release checks from a source checkout:

uv sync --dev
uv run pytest
uv run ruff check .
uv build
uv run twine check dist/*

Known Limitations

• Free API tier: 1000 requests/day rate limit
• No explicit "updated_at" field: Update detection relies on snapshot diffs, not API metadata
• LLM data only for snapshots: Media model snapshot tracking is not yet implemented
• CritPt completeness: The upstream evaluation API requires submissions for the full public problem set; this server validates object shape but cannot verify set completeness locally
• No pagination: The free API returns all models in a single response; no cursor/offset support
• Snapshot storage: Local filesystem only; no cloud sync

Attribution

This project uses data and benchmark resources from Artificial Analysis.

Attribution is required for all use of the Artificial Analysis free API. If you publish outputs, dashboards, reports, or derivative analysis using data returned by this MCP server, include attribution to artificialanalysis.ai.

CritPt benchmark evaluation data should also include attribution to the CritPt project.