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List local models / explain a selector

ollama_models
Read-onlyIdempotent

List installed Ollama models with capabilities, context window, size, and load status. Filter by capability or resident-only, refresh after pulling, and dry-run selectors to see the resolution trail.

Instructions

List the models installed on the Ollama host with their capabilities, context window, size and whether they are currently loaded. Filter with capability or resident_only; pass refresh after pulling a new model. Pass explain_selector to dry-run the model resolver — it reports exactly which model a selector would run and the full fallback chain, without spending a generation. Start there whenever routing surprises you.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hostNoOverride the base URL of the local inference server for this call only, e.g. "http://localhost:11434". Omit to use the configured host.
detailNoResponse verbosity. "concise" returns the answer plus minimal provenance; "detailed" adds the resolution trail, token counts, timings and warnings. Default is the configured value.
refreshNoBypass the cached registry snapshot and re-query the local server. Use after pulling a model.
capabilityNoOnly list models advertising ALL of these capabilities, e.g. ["vision","tools"]. Capability names come from the local server and are not a fixed set.
timeout_msNoPer-request timeout in milliseconds. Large models on a cold load can take tens of seconds before the first token, so prefer generous values over retrying.
resident_onlyNoOnly list models currently loaded in VRAM. These answer without paying a load.
explain_selectorNoDry-run a selector string (literal name, "role:NAME", or "caps:a+b") and return the resolution trail — every link tried and why it was accepted or rejected — without running inference. Use this to debug a selector before dispatching with it.
Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description reveals behavioral details: explain_selector 'without spending a generation', the reporting of the 'full fallback chain', and the need to refresh after pulling a model. This adds real context about system behavior and costs that annotations alone do not convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each serving a distinct purpose: the first states the core list function and output, the second covers filtering and refresh, and the third explains explain_selector with a usage recommendation. There is no redundant or filler content.

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

Completeness5/5

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

The description covers the primary list function, filtering, refresh, and the advanced explain_selector feature, and also describes the output fields (capabilities, context window, size, loaded status). Since there is no output schema, this is sufficient. The tool's complexity is handled well because the schema documents all parameters and the description focuses on when and how to use them.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The main description adds operational meaning: it links `refresh` to post-pull actions, connects `resident_only` to avoiding load costs, and frames `explain_selector` as the recommended debugging entry point with the 'full fallback chain' detail. This is more than just restating the schema, though not dramatically so.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'List the models installed on the Ollama host' and enumerates the output fields (capabilities, context window, size, loaded status). It also clearly introduces the second function, explain_selector, as a dry-run of the model resolver, which distinguishes it from siblings like ollama_dispatch and ollama_lifecycle.

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

Usage Guidelines4/5

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

The description provides clear conditional usage: 'pass `refresh` after pulling a new model', 'Filter with `capability` or `resident_only`', and for explain_selector, 'Start there whenever routing surprises you.' It gives context and a recommendation, but does not explicitly name alternative tools or state when not to use the tool.

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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