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Yike-Ye
by Yike-Ye

origin_bridge_status

Verify the Origin bridge's announced status and actual responsiveness, with an option to skip liveness checks when only the bridge's presence is needed.

Instructions

Report where the bridge is, and whether it is actually answering.

These are different questions, and the difference is not academic. The bridge publishes a handshake file when it starts and withdraws it when it stops -- but a modal dialog inside Origin takes over the UI thread the bridge serves on, so it can stop answering for hours while the handshake goes on saying it is up. Measured: set <dataset> -dc opens such a dialog, and nothing was served until someone clicked it.

So announced is what the file says and answering is what a request found. Only the second means the bridge can do anything. Pass check_liveness=False to skip the request when the answer does not matter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
check_livenessNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations are provided, so the description carries full behavioral burden. It discloses the handshake-file mechanism, the modal-dialog UI-thread stall, the measured consequence, and the semantic difference between announced and answering. This is far beyond what the schema alone could convey.

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

Conciseness4/5

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

The description is longer than minimal but front-loads the core purpose and every sentence earns its place. The measured example is arguably extra, but it reinforces why the announced/answering distinction matters for correct usage.

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 tool is a simple status check with one optional parameter, and the description covers its key behavioral nuance and parameter semantics. An output schema exists, so the description need not explain the return structure. Nothing critical is missing for an agent to call this tool correctly.

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

Parameters5/5

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

The schema gives no description for check_liveness (0% coverage), but the description fully explains it: 'Pass check_liveness=False to skip the request when the answer does not matter.' This adds real meaning beyond the bare boolean type and default value.

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?

States a specific verb and resource: 'Report where the bridge is, and whether it is actually answering.' The description immediately distinguishes two separate questions ('announced' vs 'answering'), which also helps separate this tool from sibling tools like origin_ping. The core purpose is unmistakable.

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?

Provides clear context about when the status matters and explicitly instructs when to skip the liveness check: 'Pass check_liveness=False to skip the request when the answer does not matter.' It does not name an alternative tool directly, but the announced-vs-answering distinction gives the agent enough context to select this tool appropriately.

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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