aistupidlevel-mcp

Model details

get_model

Read-onlyIdempotent

Retrieve detailed AI Stupid Level tracking data for one LLM—vendor, version, pricing, tool-calling support, tracking duration, and leaderboard row—by model id, name, or display name.

Instructions

Details AI Stupid Level holds on one tracked LLM: vendor, version, notes on how it is called and priced, tool-calling support, how long it has been tracked, and its leaderboard row when one has been fetched.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model": "<model>"
    },
    "name": "get_model"
  }
}

Input Schema

Name	Required	Description	Default
`model`	Yes	The model: its numeric id, its name (for example claude-opus-5-5 or gpt-6-sol) or its display name. get_leaderboard lists the tracked models with their ids.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 20, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is low, and the description clears it by disclosing a real behavioral quirk: the leaderboard row is present only 'when one has been fetched', i.e. conditionally populated. That is non-obvious and useful, though permissions and error behavior go unstated.

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?

One sentence, front-loaded with purpose before the field inventory, and every listed field earns its place by telling the agent what it can rely on being returned. The enumeration is long but not padded.

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

Completeness4/5

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

With no output schema, the description carries the burden of describing the return shape and does so by enumerating the fields, including the conditional leaderboard row. Remaining gaps (resolution when a name is ambiguous, behavior for an untracked model) are minor for a single-parameter read tool.

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

Parameters3/5

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

Schema description coverage is 100% and the schema already documents the single 'model' parameter including example names and the get_leaderboard pointer, so the description correctly adds nothing further. Baseline 3 when structured data does the heavy lifting.

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?

The description gives a specific verb (retrieves details) and a tightly scoped resource: one tracked LLM, with the exact fields returned enumerated. It does not name the nearest sibling, get_model_history, so an agent could still confuse a current snapshot with a history lookup.

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

Usage Guidelines3/5

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

No explicit when-to-use or when-not-to-use statement, and no alternative is named (get_model_history is the obvious ambiguity). Usage is only implied via the practical pointer to get_leaderboard for locating a model's id.

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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