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Check Server Provisioning Progress

check_server_provisioning
Read-onlyIdempotent

Track servers coming online after deploy_servers: reports the deployment job's state and every server in the cluster with its current state (new → provisioning → configuring → live). A server is ready once it reaches 'live', meaning CycleOS booted and the server is checking in. Pass wait_seconds to keep polling up to that bound — progress updates stream to the client while it waits — or 0 (the default) for a single snapshot. Provisioning almost always outlasts a single call: bare metal takes many minutes, so the reliable pattern is repeated snapshot calls until ready is true, not one long wait. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobNoThe deployment job ID returned by deploy_servers, to track alongside the servers.
clusterYesThe cluster whose servers to check, as passed to deploy_servers.
contextNoWhy are you calling this tool? Briefly describe the user's goal.
wait_secondsNoMax seconds to keep polling until the job completes and every server in the cluster is live. 0 returns a single snapshot. While waiting, progress updates are streamed to the client. Provisioning takes far longer than any single call can wait, so repeated 0-wait snapshots are the normal way to track it.
conversation_idNoConversation tracking id. Omit on your first tool call; every result then includes a conversation_id line — pass that exact value on all later calls in this conversation.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Goes well past the annotations: it discloses the state machine (new → provisioning → configuring → live), the readiness condition ('live' means CycleOS booted and the server is checking in), the streaming behavior while waiting, and realistic timing expectations for bare metal. 'Read-only' is redundant with readOnlyHint but consistent.

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 core purpose is front-loaded in the first clause, and each subsequent sentence carries distinct information (state machine, readiness, polling strategy). It is on the dense side and slightly repetitive about provisioning taking long, which keeps it from a 5.

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?

With no output schema, the description carries the burden of explaining returns, and it does: job state plus per-server state and the meaning of 'ready'. Combined with the polling guidance, an agent has everything needed to call and interpret this 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 coverage is 100%, so baseline is 3, but the description adds real meaning for wait_seconds: it defines 0 as a single snapshot, describes progress streaming while waiting, and warns that provisioning outlasts any single wait. The job and cluster parameters are only implied, not explained further.

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 ('Track servers coming online after deploy_servers') and scopes the return ('the deployment job's state and every server in the cluster with its current state'). It clearly distinguishes itself from deploy_servers (the predecessor) and from plain list_servers by framing itself as post-deploy progress tracking.

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?

Explicitly positions the tool in a workflow ('after deploy_servers') and prescribes the operating pattern: repeated snapshot calls until ready is true rather than one long wait. It doesn't name a competing sibling (e.g. get_deployment_status) or state when this is the wrong tool, so it stops short of a 5.

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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