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Raw JSON escape hatch

aa_raw

Retrieve raw JSON records from artificialanalysis.ai when table tools lack needed fields, using kind, query, top, and field filters. Use sparingly because responses are token-heavy.

Instructions

Raw JSON escape hatch (token-expensive — prefer the table tools).

Args: kind: model, leaderboard, providers, providers_board, media, agents, home, eval or catalog. query: slug/board/slug argument for the chosen kind (e.g. a model slug for "model", a board key for "media"/"agents", an eval slug for "eval"). fields: comma list of top-level keys to keep per record. top: records to return, capped at 5 (default 1). refresh: bypass the cache and re-fetch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNo
kindYes
queryNo
fieldsNo
refreshNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral load. It discloses two important traits: token expense and cache-bypass via refresh. It doesn't say whether it's read-only, nor whether large result sets are truncated or how errors surface, leaving meaningful gaps.

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

Conciseness4/5

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

An args list with one line per parameter is appropriately structured for a five-parameter tool. The kind line enumerates nine values in one long sentence, but nothing is wasted.

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

Completeness3/5

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

Explains inputs well but omits output shape and return semantics, and the schema offers no help (0% coverage, no output schema). For a generic escape hatch the input docs are decent, but an agent still lacks a picture of what it gets back.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate and largely does: it enumerates the valid kind values, explains query's meaning per kind with examples, defines fields for top-level projection, and notes top's cap of 5 with default 1.

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

Purpose4/5

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

Describes itself as a raw JSON escape hatch that returns unfiltered records, contrasted with a family of table tools. It's clear it's a generic low-level fetcher, though it doesn't state a single verb+resource precisely because it's a multi-kind passthrough.

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

Usage Guidelines4/5

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

Explicitly steers the agent away: 'prefer the table tools, token-expensive'. This gives a clear when-not-to-use signal. What's missing is a positive condition for when the escape hatch is actually the right choice.

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