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aa-mcp

by HenryGetz
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Python
Remote

Model leaderboard

aa_leaderboard

Rank and filter tracked AI model variants by intelligence, cost per task, speed, or value to find which model fits your budget, performance, and context needs.

Instructions

Rank and filter the ~700 tracked model variants — the default starting point.

Answers "which model is smartest / cheapest / fastest / best value right now". Returns a compact markdown table sorted by sort (default: intelligence).

Args: mode: "rank" (default), "value" (intelligence per $/task), or "frontier" (the Pareto intelligence-vs-$/task frontier — the same code path as aa_pareto, which also plots other x axes; the footnote gives the knee). sort: metric/alias to sort by — e.g. intelligence, cost_per_task, tps, ttft, context_tokens, price_in, price_out, params_b, release_date, or any benchmark key. Use aa_reference topic=fields for all names. fields: comma list of columns to show instead of the default set. top: rows to return (default 10; >15 drops the Creator column for brevity). creator: substring match on the creator/lab (e.g. "anthropic"). name: substring match on the model name/slug. open_weights: True/False to keep only open- or closed-weights models. reasoning: True/False to keep only reasoning or non-reasoning models. min_intelligence: drop models below this Intelligence Index value. max_cost_per_task: drop models above this $/task (cache-aware). min_speed: drop models below this median output tok/s. max_ttft: drop models above this median time-to-first-token (seconds). min_context: drop models with a smaller context window (tokens). released_after: ISO date (YYYY-MM-DD); keep models released on/after it. include_deprecated: include deprecated rows (default False). refresh: bypass the cache and re-fetch (use for "released today").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNo
modeNorank
nameNo
sortNointelligence
fieldsNo
creatorNo
refreshNo
max_ttftNo
min_speedNo
reasoningNo
min_contextNo
open_weightsNo
released_afterNo
min_intelligenceNo
max_cost_per_taskNo
include_deprecatedNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden and adds meaningful behavior: returns a compact markdown table sorted by sort, default intelligence; top >15 drops the Creator column; refresh bypasses cache; frontier mode shares code path with aa_pareto. It stops short of stating read-only nature, auth needs, or rate limits, so not full 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads purpose and user questions before the Args list, and the Args block is tightly formatted with no redundant sentences. Length is justified by the 16 parameters.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 16 parameters, no annotations, and no output schema, the description covers output format, default sort, column behavior, caching, and all parameters. It leaves no critical gap for an agent to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does: all 16 parameters are documented with defaults, allowed values, units, examples, and semantics (e.g., max_cost_per_task cache-aware, min_speed median output tok/s). It also points to aa_reference for the full sort alias list.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Rank and filter the ~700 tracked model variants') and positions itself as the default starting point. Answers concrete user questions ('which model is smartest / cheapest / fastest / best value right now') and distinguishes itself from aa_pareto and aa_reference where relevant.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says this is the default starting point for leaderboard queries and names alternatives for specific needs: aa_reference for field names, aa_pareto for other x-axis plots. Mode descriptions clarify when to use rank, value, or frontier.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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