get_global_indexCheck whether AI models are worse today: returns a 0-100 global index for tracked models, its 24-hour trend, and how many are performing well.
AI Stupid Level's global index: one 0-100 number for the tracked models together, now and over the last 24 hours, with its trend and how many models are performing well. Use it for "are AI models worse today?".
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Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is fully covered elsewhere. The description usefully adds the temporal window (now + last 24 hours) and the output value semantics, but says nothing about freshness/caching, refresh cadence, or what threshold counts as 'performing well'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, both front-loaded with the payload description first and the routing hint second. Every clause carries information — the 0-100 scale, the two time horizons, trend, and model count — with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no parameters, the description is the only place return values can be conveyed, and it does state the core fields (index value, trend, healthy-model count). It is nearly complete, though it omits the exact response shape and any hint about refresh timing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so there is no parameter semantics to explain; per the rubric this is the baseline 4. Nothing in the description contradicts or confuses the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states concretely what the tool returns: a single 0-100 aggregate score for tracked models, both current and over the last 24 hours, plus trend and a count of well-performing models. This is specific enough to distinguish it from per-model tools like get_leaderboard, but it never names or explicitly contrasts a sibling, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives a concrete triggering question — 'are AI models worse today?' — which tells the agent what class of user intent should route here. There are no exclusions (e.g., when to prefer get_drift_status or get_leaderboard instead), so it lacks the when-not guidance needed for a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.