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ParalonCloud

paraloncloud-rentals

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by ParalonCloud

get_rental

Retrieve a rental's status and Jupyter connection URL with token. Waits until the rental is ready (up to 24 seconds) then returns the URL, avoiding repeated polling.

Instructions

Get one rental's status and connection details (Jupyter URL + token). After create_rental, call this to get the URL. It WAITS internally (up to ~24s) for the rental to finish coming up and returns as soon as it's ready — so you usually only need ONE call, at most two. Do NOT call it repeatedly in a tight loop. Provisioning plus the secure tunnel take ~20-40 seconds total; the URL is ready when connection_pending is false. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rental_idYes
Behavior5/5

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

With no annotations provided, the description carries the full burden and does an excellent job. It discloses the blocking/waiting behavior, the ~24s internal timeout, the total provisioning time (~20-40s), the connection_pending readiness flag, and declares the tool 'Read-only.' This gives an agent a clear mental model of side effects and timing.

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?

Every sentence adds unique value: purpose, usage timing, wait behavior, anti-loop guidance, timing estimate, readiness condition, and read-only flag. The content is front-loaded and the length is justified for the behavioral nuance it conveys.

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?

For a one-param tool with no output schema, the description covers what the tool returns (status, Jupyter URL, token), when it's ready, how long to wait, and how to interact with the rental lifecycle (create → get → destroy). It is fully complete for the agent's needs.

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 0%, so the description must explain rental_id. It does so indirectly: 'Get one rental's status' and 'After create_rental, call this' implies the rental_id comes from create_rental. While not a formal param definition, this is sufficient context for a single obvious parameter. It earns above baseline.

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 uses a specific verb ('Get') and resource ('one rental's status and connection details (Jupyter URL + token)'), clearly distinguishing this from sibling list_rentals. It immediately conveys the tool's unique purpose.

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 provides when-to-use guidance: 'After create_rental, call this to get the URL.' It also gives strong behavioral guidance: warns against tight loops, explains the internal wait (~24s), and states the readiness condition. This is far beyond a minimal description.

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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