trending_modelsTracked AI models ranked by Heat Score (0–100, weighted percentiles of measured Hugging Face/OpenRouter signals) with the raw signals, local-run facts (GGUF size, quantization) and links. Models still collecting a week of history have heat=null and rank after scored ones.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | No | Return a single model by slug. | |
| limit | No | How many models to return (default 12). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It explains the Heat Score calculation basis, the meaning of heat=null for models with less than a week of history, and that such models rank after scored ones. This goes well beyond a minimal description, though it does not cover response format details or side effects.
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?
The description is information-dense but efficient, with the main point front-loaded and the null-ranking edge case in a second sentence. The first sentence is somewhat heavy with parentheticals, but every phrase earns its place.
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?
Given the simplicity of the tool and the absence of an output schema, the description covers the important return concepts: ranking metric, raw signals, local-run facts, links, and null-heat handling. It does not spell out the exact response shape or sort direction, but for a low-complexity list-like tool this is reasonably complete.
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?
Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific detail beyond the schema; both slug and limit are already well documented in the input schema, so no extra semantic guidance is needed.
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 names a specific resource (tracked AI models) and the key behavior (ranked by Heat Score), clearly distinguishing this from the hardware/GPU-focused siblings like gpu_prices and list_hardware. Even though no explicit sibling comparison is made, the domain and content are unambiguous.
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?
Usage is implied: an agent would call this when it needs trending model rankings or model comparison data. However, there is no explicit statement about when to choose this tool over find_fit, gpu_prices, or list_hardware, and no exclusions or alternative routing guidance.
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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Each tool targets a distinct concern: model discovery, hardware profiles, GPU rental pricing, and hardware/model fit analysis. There is no functional overlap, and the documented dependency between list_hardware and find_fit is clear.
The names are short, lowercase, and snake_case, with find_fit and list_hardware using clear verb_noun form. gpu_prices and trending_models are noun-phrase names rather than verb-initial commands, creating a minor stylistic deviation, but the intent remains predictable.
Four tools is well-scoped for an informational radar service; each tool covers a necessary query without redundancy or bloat. The set feels complete without being overwhelming.
The tool set covers the main workflows: viewing trending models, checking hardware profiles, obtaining GPU prices, and evaluating model fit on specific hardware. Minor gaps exist around detailed single-model or single-hardware lookups, but agents can work around these using the provided list and ranking tools.