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Sats4AI - Bitcoin-Powered AI Tools

by io.github.cnghockey
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get_job_result

Read-onlyIdempotent

Retrieve the final output of a completed async job. Call ONLY after check_job_status returns status='COMPLETED' — calling on a job that has not completed returns an error. Returns JSON whose shape depends on jobType: video/video-image → { videoUrl, duration }; image-3d → { modelUrl } (GLB format); transcription → { text, language, segments }; epub-audiobook → { url, annexeUrl, totalChapters, completedChapters } (annexeUrl is a companion PDF of equations, tables, addresses, keys and hashes the narration replaces with a spoken marker; null when the book had none); ai-call → { transcript, duration, summary }. All URLs are temporary (valid ~1 hour) — download immediately. This tool is free and does not require payment. Do NOT use for synchronous tools — those return results directly.

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.
paymentIdNoThe paymentId that bought this job. On a failed job the refund is opened either way; the claim link (lnurl_withdraw / refund_secret) is returned only when this matches — or call check_payment_status(paymentId).
requestIdYesThe requestId returned by the original async tool — same ID used with check_job_status

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / paymentId
      Added value: +{
+  "description": "The paymentId that bought this job. On a failed job the refund is opened either way; the claim link (lnurl_withdraw / refund_secret) is returned only when this matches — or call check_payment_status(paymentId).",
+  "type": "string"
+}
  2. Changed1 schema field changed
    • changedInput schema / properties / jobType / enum
      Previous value: -[
-  "video",
-  "video-image",
-  "video-fal-standard",
-  "video-fal-pro",
-  "image-3d",
-  "transcription",
-  "transcribe-translate",
-  "epub-audiobook",
-  "ai-call"
-]New value: +[
+  "video",
+  "video-image",
+  "video-fal-standard",
+  "video-fal-pro",
+  "image-3d",
+  "transcription",
+  "transcribe-translate",
+  "translate-epub",
+  "epub-audiobook",
+  "ai-call"
+]
  3. Changed1 schema field changed
    • changedInput schema / properties / jobType / enum
      Previous value: -[
-  "video",
-  "video-image",
-  "video-fal-standard",
-  "video-fal-pro",
-  "image-3d",
-  "transcription",
-  "epub-audiobook",
-  "ai-call"
-]New value: +[
+  "video",
+  "video-image",
+  "video-fal-standard",
+  "video-fal-pro",
+  "image-3d",
+  "transcription",
+  "transcribe-translate",
+  "epub-audiobook",
+  "ai-call"
+]
  4. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint; the description adds significant behavior beyond them: premature calls error, temporary URLs expiring after about an hour, and that the tool is free. There is no contradiction with annotations.

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 front-loaded with purpose and usage guidance, and every remaining sentence earns its place: output shapes, URL expiry, free status, and sync-tool exclusion. The length is justified because no output schema exists.

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

Completeness3/5

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

This is rich but incomplete: the jobType enum includes 10 values, yet the return-shape mapping covers only 5 buckets and omits video-fal-standard/video-fal-pro, transcribe-translate, and translate-epub. Since there is no output schema, those shape gaps are meaningful for an agent invoking those variants.

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 100%, so baseline is 3. The description does not add parameter-level semantics beyond mapping jobType to return shape, while the schema already explains requestId, jobType, and paymentId. Thus the description neither needs to compensate heavily here.

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?

Starts with a specific verb and resource: 'Retrieve the final output of a completed async job,' which clearly separates it from status-checking and synchronous tools. It clearly identifies the tool as the result-fetch step in the async workflow.

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 states the precondition: 'Call ONLY after check_job_status returns status="COMPLETED"' and warns that early calls return an error. It also says not to use it for synchronous tools, directly naming the alternative behavior.

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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