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list_models

Lists available models from opencode: providers, effort levels, context windows, reasoning support, and aliases. Use it to select a model before delegation and find valid effort values.

Instructions

List what this opencode server actually offers: every provider/model, its effort levels (opencode calls them "variants"), context window and whether it reasons - plus the aliases configured in bridge.config.json.

USE IT BEFORE delegate_task whenever the user has not named a model: pick a candidate here, then propose it to them with a one-line reason. It is also how you find the valid 'effort' values, which differ per model.

The catalog is large (hundreds of models across providers), so the output is capped. It always reports how many models matched and how many were left out - if something you expect is missing, narrow with 'query'/'provider' or raise 'limit' instead of assuming it is not there.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many rows to print (default 40)
queryNoFree-text filter over "provider/model" and the display name, e.g. "qwen", "gemini", "codex"
providerNoExact provider id, e.g. "opencode-go", "openrouter", "google"
reasoning_onlyNoOnly models that support reasoning
Behavior5/5

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

Discloses that output is capped, always reports how many models matched and were left out, and warns not to assume missing models are absent. With no annotations, this is essential behavioral context.

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?

Two compact paragraphs with no filler. Every sentence adds meaningful information about purpose, usage, or behavior.

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?

Despite no output schema or annotations, the description explains the return content, output cap, count reporting, and usage context with delegate_task, covering all needed information for a 4-param, zero-required tool.

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 covers all 4 parameters with descriptions, so baseline is 3. The description adds context on using query/provider to narrow and limit to raise, which enriches parameter usage beyond the schema.

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 clearly states it lists what the opencode server offers: providers/models, effort levels, context window, reasoning, and aliases. It explicitly positions itself relative to delegate_task, distinguishing it from sibling tools.

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

Usage Guidelines5/5

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

Explicitly instructs to use before delegate_task when the user hasn't named a model, and to find valid effort values. Also provides guidance on narrowing results with query/provider or raising limit.

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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