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auto

Automatically searches for the right server, picks it, infers arguments, and executes the task. Ideal when you are unsure which server to use.

Instructions

search → pick server → infer args → call. Use when you don't know which server to use.

auto('what time in Tokyo') auto('list issues on acme/api', server_hint='github')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYes
tool_nameNo
argumentsNo
server_hintNo
keysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It describes the workflow (search, pick server, infer args, call) but does not mention potential side effects, error behavior, or whether calls are read-only or destructive. The agent is left guessing about safety and outcomes.

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 extremely concise: two sentences plus two examples. Every word adds value, no fluff. The examples are well-chosen and demonstrate usage patterns effectively.

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

Completeness2/5

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

Given the tool's complexity (automatic server selection, argument inference, calling other tools), the description is too sparse. It lacks details on return values (though an output schema exists), error handling, and the inference mechanism. Sibling tools like 'search' and 'call' are not explained in context.

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

Parameters2/5

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

With 0% schema_description_coverage, the description should clarify parameter meanings. It only hints at 'task' and 'server_hint' via examples but does not explain 'tool_name', 'arguments', or 'keys'. The schema lists these but provides no descriptions, leaving the agent underinformed.

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's purpose: an auto-routing orchestrator that searches for a server, picks it, infers arguments, and calls. Examples clarify usage with natural language tasks. It distinguishes from sibling tools like 'search' and 'call' by automating server selection.

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 explicitly states when to use: 'Use when you don't know which server to use.' It also provides illustrative examples. However, it does not specify when not to use or mention alternative tools like 'call' for direct invocation.

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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