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app_status

Build/run status of a container app deployed via publish_site with a Dockerfile. Poll until status is live or failed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
logsNoInclude build logs
slugYesSite slug

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description must reveal behavior. It discloses that status is pollable and reaches terminal states live/failed, and that it's tied to Dockerfile deployments. But it leaves ambiguity about whether the tool blocks until terminal or returns immediately for the caller to poll, and doesn't mention any side effects or edge cases.

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?

Two short sentences, front-loaded with purpose and a clear polling instruction. No redundant text.

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

Completeness4/5

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

For a simple 2-parameter status tool, the description covers deployment context and expected statuses without needing an output schema. The only notable gap is the ambiguous polling behavior (blocking vs. non-blocking), which keeps it from a 5.

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?

The schema already describes slug and logs with 100% coverage, so the baseline is 3. The description adds no extra parameter semantics beyond the schema, merely reinforcing the deployment context.

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 identifies the tool as retrieving build/run status for a container app deployed via publish_site, distinguishing it from more general site tools like get_site. 'Poll until status is live or failed' gives an explicit action and expected terminal states.

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 specifies the context: for apps deployed via publish_site with a Dockerfile, and instructs polling until live/failed. However, it doesn't explicitly name alternatives or exclusion cases (e.g., when to use get_site instead), so it falls 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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TDQS

A3.6/5.0
Disambiguation5/5

Each tool targets a distinct resource and action. Site, drive, variable, and auth operations are clearly separated, and even closely related drive tools (share, tokens, revoke_token) have unambiguous purposes.

Naming Consistency3/5

Naming mixes verb_noun (publish_site, create_drive), resource_verb_noun (drive_read_file, drive_delete_file), and noun-only (app_status, platform_info) conventions. While still readable, the pattern is not uniform across resource types.

Tool Count4/5

26 tools is above the typical well-scoped range, but the platform covers multiple domains: sites, drives, variables, and auth. Each tool has a clear role, so the count feels justified by the breadth rather than redundant.

Completeness4/5

The surface provides comprehensive CRUD and lifecycle coverage for sites, drive files, variables, and auth. The notable gap is the absence of a delete_drive tool, which is a minor dead end but does not significantly impair core workflows.