Apiframe

@apiframe check my account balance and list the available image generation models with their current pricing

Your Apiframe account shows 2,450 credits remaining under the Team plan. Currently available image models include: Flux Pro (8 credits per generation, supports aspect ratios 1:1 through 21:9, max resolution 2048×2048), SDXL Lightning (3 credits, fixed 1024×1024), and Midjourney v6 (12 credits, supports style presets and chaos parameter 0-100). Each model exposes different control parameters—Flux Pro offers guidance scale 1-20 and seed control, while Midjourney v6 adds stylize and weird parameters.

This example demonstrates read-only account introspection and model discovery—essential first steps before submitting paid generation jobs. Users should call List Models before every generation session because pricing and available controls change as Apiframe adds providers. No credits are consumed by these lookup calls.

@apiframe generate a product hero image using Flux Pro: 'minimalist smartwatch on marble surface, studio lighting, commercial photography' at 16:9 aspect ratio

Image generation job submitted (job_id: img_9x4k2p). This request will consume 8 credits from your balance. The job is currently processing with Flux Pro—typical completion time is 15-45 seconds depending on queue depth. Once complete, the result will include a high-resolution PNG URL valid for 7 days and metadata showing the actual seed used, allowing you to reproduce or iterate on this exact output with parameter adjustments.

This write example shows the two-step async pattern: Generate Image returns a job ID immediately, then Get Job polls for completion. The MCP handles the credit deduction automatically—failed jobs refund credits, but users should verify their balance first. The 7-day URL expiration means teams must download and store final assets promptly.

@apiframe review my last 10 generation jobs and summarize which models performed best for product photography based on cost and output quality

Analyzing your recent jobs: 6 used Flux Pro (avg 8 credits, 100% success rate, median completion 22s), 3 used SDXL Lightning (avg 3 credits, 100% success, median 8s), and 1 Midjourney v6 job (12 credits, completed in 38s). For product photography specifically, your Flux Pro jobs consistently delivered commercial-grade results with fine detail in reflections and textures, while SDXL Lightning traded some sharpness for 3× faster turnaround. Cost-per-successful-image favors SDXL Lightning at 3 credits, but your team re-generated Flux Pro outputs less often, suggesting better first-attempt quality offsets the higher per-job cost.

This synthesis example pairs List Jobs data with AI reasoning to extract workflow insights. The MCP provides raw job metadata (status, cost, timestamps, model) but cannot assess subjective quality—the AI infers performance from your re-generation patterns. Useful for optimizing model selection across a team's workload, though it requires sufficient job history to be meaningful.

When Apiframe beats design handoffs for small marketing teams

A 3-person growth team running Meta and Google ads needs 20 image variants per campaign, twice a week. Apiframe wins here because the MCP lets you batch-generate images directly in Switchy, check credit balance before each run, and poll job status without leaving the workspace. The List Models tool surfaces pricing up front, so you know a FLUX Pro batch will cost 40 credits before you commit. The trade-off: if your team already has a Figma workflow and a designer on retainer, Apiframe adds cost without saving time. But if you're buying stock photos or waiting on Fiverr, this MCP turns a 2-day cycle into a 20-minute sprint. Pull the trigger if you're spending more than $200/month on static creative and your campaigns need speed over pixel-perfect brand control.

Apiframe for explainer videos when Loom isn't enough

A 6-person product team at a B2B SaaS company ships feature updates every two weeks and needs a 30-second explainer video for each launch. Apiframe's Generate Video tool handles this if your demos are concept-driven (animated UI flows, abstract product benefits) rather than screen recordings. The MCP's Get Job polling means you can kick off a generation during standup and have the result in Slack by lunch. The boundary: video generation burns 100+ credits per job depending on the model, and if your output needs voiceover sync or brand-specific motion graphics, you'll still need a video editor downstream. This works when your demos are formulaic and your team would otherwise skip video entirely because production is too slow. If you're already using Descript or CapCut, Apiframe is redundant.

When Apiframe's music generation replaces royalty-free libraries

A 4-person content team publishes a weekly podcast and needs unique intro music for each episode to avoid Spotify's repetitive-audio flags. Apiframe's Generate Music tool creates 30-second tracks on demand, and the MCP's List Jobs filter lets you archive past generations by episode name. The cost is predictable—most music models run 20-50 credits per job—and the Get Account tool shows your balance before you generate. The catch: if your podcast has a signature sound or your audience expects a consistent brand jingle, generative music won't match a custom composition. This MCP is the right call when you're currently paying $15/month for Epidemic Sound but only using 4 tracks, or when you're spending an hour per episode searching royalty-free libraries. Skip it if your intro is part of your brand identity.