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Leev1s

aa-mcp

by Leev1s
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
AA_MCP_LOG_LEVELNoLog level (DEBUG, INFO, WARNING, ERROR) (default: INFO)
AA_MCP_SNAPSHOT_DIRNoDirectory for update snapshots (default: ~/.local/share/aa-mcp/snapshots/)
ARTIFICIAL_ANALYSIS_API_KEYYesYour Artificial Analysis API key (get one free at https://artificialanalysis.ai/account)

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": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
aa_list_llmsA

List LLM models from Artificial Analysis with optional filtering and sorting.

Args:
    creator: Filter by creator name (case-insensitive substring match)
    name: Filter by model name (case-insensitive substring match)
    slug: Filter by slug (case-insensitive substring match)
    sort_by: Sort key - 'intelligence', 'price', 'speed', 'ttft', 'coding', 'math'
    limit: Max models to return (default 20)

Returns:
    JSON array of model summaries.
aa_get_modelA

Get detailed information about a single LLM model by id, slug, or name.

Args:
    identifier: Model id (UUID), slug, or name. Partial matching supported.

Returns:
    JSON object with full model details, or candidates if multiple matches.
aa_compare_modelsA

Compare multiple LLM models side by side on key metrics.

Args:
    identifiers: List of model ids, slugs, or names (at least 2).

Returns:
    JSON object with comparison table and per-model details.
aa_list_recent_updatesA

Detect recent LLM model changes by comparing current data to the last local snapshot.

Identifies new models, removed models, and field-level changes (pricing, speed,
intelligence scores, etc.). On first run, saves a baseline snapshot and reports
the full model list as "initial baseline".

Args:
    save_new_snapshot: If true (default), save the current data as the new snapshot
                      after diffing. Set false to preview-only.

Returns:
    JSON with structured diff: added, removed, changed models with field-level deltas.
aa_list_media_modelsA

List top-ranked multimodal / media models by Elo ratings.

Args:
    modality: One of 'text-to-image', 'image-editing', 'text-to-speech',
              'text-to-video', 'image-to-video'
    top_n: Number of top models to return (default 10)
    include_categories: Include per-category Elo breakdown (text-to-image, text-to-video only)

Returns:
    JSON array of media model rankings.
aa_evaluate_critptA

Submit a complete CritPt benchmark batch for official evaluation.

The upstream endpoint requires submissions for all public CritPt problems in
one request. Each submission should include problem_id, generated_code,
model, and generation_config. This tool can take substantial time because
the upstream grading service runs benchmark evaluation jobs.

Args:
    submissions: Complete list of CritPt submission objects.
    batch_metadata: Optional metadata for the batch.

Returns:
    JSON response with accuracy, timeout rate, and judge/server error counts.
aa_healthcheckA

Verify API key validity and upstream API reachability.

Returns:
    JSON with connectivity status, model count, and any error details.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.2/5.0

Scored across 7 tools

Disambiguation5/5

Each tool targets a distinct operation: healthcheck for connectivity, list_llms for listing models, get_model for details, compare_models for comparison, list_media_models for media models, list_recent_updates for tracking changes, and evaluate_critpt for benchmark submission. No overlap or ambiguity.

Naming Consistency5/5

All tools use the 'aa_' prefix with consistent snake_case naming. Most follow a verb_noun pattern (list_llms, get_model, compare_models, evaluate_critpt, list_media_models, list_recent_updates), with only healthcheck being a single word, which is acceptable.

Tool Count5/5

With 7 tools, the set is well-scoped for an LLM model comparison and evaluation API. It covers essential operations (listing, details, comparison, benchmark submission, updates tracking, healthcheck) without unnecessary bloat or missing core functionality.

Completeness4/5

The tool surface covers listing, detail, comparison, media models, updates, and evaluation submission. A minor gap is the lack of a tool to retrieve past evaluation results or browse problems, but the core workflow is supported.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources