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Cloady

Cloady

Official
by Cloady

Stream Deploy Logs

streamDeployLogs
Read-only

Fetch and stream deploy logs from Cloady, returning a complete log snapshot or live updates every two seconds until the deployment completes.

Instructions

Returns one {kind:"snapshot", log, status} frame — the whole log so far, not a delta: the stored log for a finished deploy, the live build pod's log for the in-flight one. Sending Accept: text/event-stream instead re-sends a growing snapshot every two seconds; a finished deploy sends one frame and closes, the in-flight one keeps the connection open after it ends, so close it yourself once a frame carries final: true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
envNo
regionNo
appSlugYes
deployIdYes
workspaceSlugYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral detail beyond annotations: the whole-log-not-delta behavior, the streaming re-send every two seconds, the connection-closing rule, and the `final: true` flag. This is rich, non-contradictory context that an agent needs to interact correctly.

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 two sentences with zero waste. The core behavior is front-loaded, followed by the streaming alternative and connection-handling details. Every clause adds necessary information without redundancy.

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 streaming tool with no output schema, the description covers the essential operational details: frame structure, streaming behavior, connection lifecycle, and the `final` flag. It is complete enough for an agent to call the tool correctly and interpret the stream. No critical information is missing.

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

Parameters3/5

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

Schema description coverage is 0%, so the description should compensate for parameter meaning. It does not explain `env`, `region`, or the required slugs beyond their names, which are fairly self-explanatory. The description focuses on behavior rather than parameter semantics, providing only minimal value over the schema's names and enum for `env`.

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 clearly states it returns deploy log frames with a specific structure (`{kind:"snapshot", log, status}`) and explains the snapshot vs delta nature. It distinguishes from sibling streaming tools by focusing on deploy logs and the two modes (single snapshot vs growing stream). The verb 'Returns' is specific, and the resource is unambiguous.

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?

It gives clear context on when to use this tool (to retrieve deploy logs) and describes the two modes (single frame vs streaming) with explicit instructions on when to close the connection. However, it does not explicitly name alternative tools or state when not to use it, leaving exclusions to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.