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Sats4AI - Bitcoin-Powered AI Tools

await_result

Read-onlyIdempotent

Wait for an async job to finish and return its result in ONE call — no manual polling loop. Pass the requestId + jobType from an async tool (generate_video, animate_image, generate_3d_model, transcribe_audio, epub_to_audiobook, ai_call). If your MCP client opened the request with 'Accept: text/event-stream', this streams live progress (notifications/progress) while it waits, then returns the final result; otherwise it does a single status check and returns immediately (call again until status='COMPLETED'). For long jobs it waits up to ~4 minutes per call, then returns status='IN_PROGRESS' with timed_out=true — call again with the same requestId to keep waiting. Equivalent to check_job_status + get_job_result combined. Free; no payment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobTypeYesMust match the async tool that returned requestId.
requestIdYesThe requestId returned by the async tool.

TDQS

A4.7/5.0
Behavior5/5

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

Despite annotations already declaring idempotent and read-only hints, the description adds substantial behavioral context: it waits up to ~4 minutes, streams progress when the client accepts text/event-stream, otherwise does a single status check, and returns timed_out=true with status IN_PROGRESS when the job isn't finished. It also clarifies that the operation is free, which is extra useful context. 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 dense but well-structured, with the main action and benefit front-loaded in the first clause. Every sentence contributes: input requirements, streaming behavior, timeout behavior, and equivalence to sibling tools. The 'Free; no payment' note is a minor but useful addition, not clutter.

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 there is no output schema, the description adequately explains what the caller receives: either a streamed final result, an immediate status check result, or a timed-out response with instructions to call again. It also explains how to use it with specific async tools and how streaming affects behavior, covering essentially all context an agent needs to invoke it correctly.

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 provides 100% coverage for both parameters, including the constraint that jobType must match the async tool that returned requestId. The description reinforces this with a list of source tools, but does not add material new meaning beyond what the schema describes. Baseline 3 is appropriate because the schema carries the semantic weight.

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 opens with a specific action: 'Wait for an async job to finish and return its result in ONE call — no manual polling loop.' It clearly identifies the resource (async job result) and distinguishes itself from siblings like check_job_status and get_job_result by being their combined equivalent.

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?

Explicit guidance is given on when to use: after any async tool, pass the requestId + jobType. It also names the exact async tools that produce such requests, and explains the streaming vs. non-streaming behavior differences, plus the call-again-until-done pattern. The 'Equivalent to check_job_status + get_job_result combined' sentence helps the agent choose it over those siblings.

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.