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Sats4AI - Bitcoin-Powered AI Tools

by io.github.cnghockey

check_job_status

Idempotent

Poll the status of an async job. Use this after calling any async tool (generate_video, animate_image, generate_3d_model, transcribe_audio, epub_to_audiobook, ai_call) that returns a requestId. Returns JSON: { status: 'IN_PROGRESS' | 'COMPLETED' | 'FAILED', requestId, jobType }. For epub-audiobook, also includes progress (0-100) and chapterProgress array. Poll every 5-10 seconds. When status is 'COMPLETED', call get_job_result to retrieve the output. When status is 'FAILED', the response includes an error message — do not retry automatically. This tool is free and does not require payment. Do NOT use for synchronous tools (generate_image, generate_text, etc.) — those return results immediately.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobTypeYesMust match the async tool: video=generate_video, video-image=animate_image, image-3d=generate_3d_model, transcription=transcribe_audio, epub-audiobook=epub_to_audiobook, ai-call=ai_call. video-fal-standard/video-fal-pro = the FAL fallback jobType generate_video returns when Replicate is at capacity.
requestIdYesThe requestId returned by the async tool (e.g., from generate_video, animate_image, generate_3d_model, transcribe_audio, epub_to_audiobook, ai_call)

TDQS

A4.7/5.0
Behavior5/5

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

The description transparently explains the polling behavior, that it returns status and additional progress for epub-audiobook, and that it should not be used for synchronous tools. It aligns with the idempotentHint annotation by implying safe repeated calls.

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 dense but each sentence adds value: purpose, applicable tools, return format, extra progress, polling interval, next steps, and exclusions. It is well-structured and not verbose.

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?

Given the absence of an output schema, the description fully explains the return object, including status values and extra fields. It also covers the full workflow from polling to result retrieval and error handling, making it complete for the user.

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 input schema already provides detailed descriptions for both parameters, including the jobType mapping. The tool description does not add new parameter information beyond what is in the schema, so the baseline of 3 is appropriate.

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 the tool polls the status of an async job, distinguishing it from siblings like get_job_result and await_result. It names the async tools it applies to, making the purpose unambiguous.

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 tells when to use (after async tools) and when not to use (synchronous tools). Provides polling interval, next steps on COMPLETED, and advice not to retry on FAILED. This is comprehensive guidance.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap, especially among call tools (ai_call, place_call, open_voice_bridge) and image generation/editing tools (generate_image, edit_image, animate_image). Descriptions help differentiate, but an agent might still select the wrong one.

Naming Consistency4/5

The vast majority of tools follow a verb_noun pattern (e.g., generate_image, send_sms). A few exceptions exist (await_result, check_job_status, epub_to_audiobook) but the overall pattern is strong and predictable.

Tool Count3/5

With 50 tools, the server is very extensive. While each tool earns its place given the broad scope of AI services, the count feels high and could overwhelm agents, making selection less efficient.

Completeness5/5

The tool surface is remarkably comprehensive, covering generation, editing, conversion, communication, async management, payments, and error handling. There are no obvious gaps for the stated Bitcoin-powered AI toolkit purpose.

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