List local models

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

Annotations already cover the safe-read profile (readOnly, idempotent, non-destructive, closed-world), and the description adds substantial operational context beyond them: requires Ollama reachable, runs under a second in two listing calls, loads no model, and the result is a snapshot that goes stale after keep_alive. It even describes the output line format and sort order.

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?

Front-loaded with what the tool reports, then usage, then return format and constraints. Dense and largely waste-free, though the return-format paragraph is verbose for what it conveys.

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, the description carries the full burden of explaining returns and does so explicitly (machine line, 'N models pulled:', per-model lines with LOADED/VRAM verdicts). Nothing needed to call it correctly is missing.

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?

Zero parameters, so the schema baseline applies. The description confirms this by stating it takes no arguments, no model name and no filter, which prevents an agent from inventing filters, but it adds nothing beyond that confirmation.

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?

States a specific verb and resource (lists Ollama models pulled on this machine) plus what is reported: size, parameters, quantisation, load state, context, VRAM fit. It explicitly distinguishes itself from siblings by noting it knows nothing about past calls, routing those to diagnose_my_turns and read_my_last_turn.

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?

Gives explicit timing guidance ("Use it before choosing a model or a context length", "call it right before deciding") and names the alternative tools for a different need (past calls). The when-not is clear: not for historical call data, and no filtering is possible.

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