tsf_list_models

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

Annotations already mark the tool as readOnly, idempotent, and non-destructive. The description adds important behavioral details beyond that: the first call may take ~30 seconds to import TimeCopilot, later calls are instant; it returns structured output with real reasons for unavailability (including Python version gating). No contradictions.

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 compact and structured: first sentence states purpose, second gives usage guidance, third explains return structure, and fourth notes startup latency. Every sentence adds essential information, with no redundancy.

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 an output schema exists (the agent can rely on structured return type details), the description covers all necessary context: what the tool does, when to use it, its runtime behavior (latency), and the high-level shape of results. No gaps remain for a list/probe tool.

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 input schema contains descriptions for both parameters (family and include_unavailable) that are self-explanatory. The tool description does not add new parameter information—it only repeats that statistical models are cheap and always installed, which already appears in the schema. Schema coverage via inline descriptions is present, so a baseline score of 3 is appropriate.

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 explicitly states 'Probe which models actually import in this environment' and specifies the tool's role in preventing proposal of non-running models. It clearly distinguishes itself from siblings like cross_validate by giving a precise pre-check use case.

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?

The description contains an explicit directive: 'Call this before cross-validating so you never propose a model that cannot run here.' It also notes that statistical models are always installed, which helps with decision-making. No alternatives are listed, but the use case is crystal clear.

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