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llm_providers

View supported AI providers and check configuration status to verify available routing options for your AI tasks.

Instructions

List all supported providers and which ones are configured.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context by distinguishing between 'supported' (available) and 'configured' (active) providers, revealing this is a state-inspection tool. However, it omits other behavioral details like whether results are cached or if the operation requires specific permissions.

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 a single, efficient sentence of nine words with no redundancy. It is front-loaded with the action (List) and immediately specifies the dual nature of the returned information (supported vs configured).

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

Completeness4/5

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

Given the tool's low complexity (no parameters) and the presence of an output schema, the description is adequately complete. It clarifies what information the output contains without needing to detail return values, though it could briefly mention this is a discovery/utility function.

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?

The tool has zero parameters, establishing a baseline score of 4 per the rubric. The description appropriately focuses on the tool's output behavior rather than non-existent inputs.

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

Purpose4/5

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

The description clearly states the tool 'Lists all supported providers and which ones are configured,' specifying the verb (List) and scope (providers, configuration status). It effectively distinguishes itself from action-oriented siblings like llm_generate or llm_setup by focusing on discovery and state inspection.

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

Usage Guidelines2/5

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

The description provides no guidance on when to invoke this tool versus alternatives. It does not indicate whether this should be called before other operations (e.g., to check available providers before generation) or how it relates to llm_setup.

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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