MCP SERVER
weftly
# Weftly Pay-per-job audio/video processing for AI agents. Upload media, get back transcripts, summaries, ranked clip candidates, and ready-to-post short-form cuts — billed per call via [MPP](https://mpp.directory/) (Tempo USDC) or Stripe Checkout for browser MCP hosts. ## Tools - **`transcribe`** — SRT transcript from any audio/video file. _$0.50 audio · $1.00 video_ - **`summarize`** — long-form summary + transcript. _$0.75 audio · $1.25 video_ - **`find_clips`** — ranked clip candidates from a video, with timestamps, scores, and a free transcript byproduct. Optional `query` for needle-in-haystack mode. _$2.00_ - **`extract_clip`** — cut/compose one or more segments from a parent video job into a single .mp4 (with optional time-shifted transcript). _$0.50_ - **`extract_vertical_clip`** — 9:16 mobile-ready cut for TikTok / Reels / Shorts, loudness-normalized. _$0.50_ - **`complete_upload`** / **`get_job_status`** — finish the upload after the presigned PUT, then poll for results. *More coming soon* ## How it works 1. Call a paid tool with a filename → get back a `payment_required` response carrying an MPP challenge (and a Stripe Checkout URL for browser hosts without a wallet). 2. Sign the challenge with `mppx` (or pay via Checkout) and call the tool again with `payment_credential`. 3. Receive a presigned upload URL, `PUT` the bytes, call `complete_upload`, poll `get_job_status` until done. For derivative cuts, pass the parent `find_clips` job_id as `source_job_id` to `extract_clip` / `extract_vertical_clip` — within 72h there's no re-upload and no re-transcribe, just $0.50 per cut. ## Endpoints - MCP: `https://api.weftly.ai/mcp` (Streamable HTTP, JSON-RPC 2.0) - Web UI: `https://chat.weftly.ai` - Discovery: `/.well-known/mpp.json` · `/.well-known/openapi.json` · `/.well-known/cards/{tool}.md` Failed jobs auto-refund. Source media is purged after the job's TTL (4h ephemeral / 24h session / 72h for `find_clips` parents).
What it provides
# Weftly Pay-per-job audio/video processing for AI agents. Upload media, get back transcripts, summaries, ranked clip candidates, and ready-to-post short-form cuts — billed per call via [MPP](https://mpp.directory/) (Tempo USDC) or Stripe Checkout for browser MCP hosts. ## Tools - **`transcribe`** — SRT transcript from any audio/video file. _$0.50 audio · $1.00 video_ - **`summarize`** — long-form summary + transcript. _$0.75 audio · $1.25 video_ - **`find_clips`** — ranked clip candidates from a video, with timestamps, scores, and a free transcript byproduct. Optional `query` for needle-in-haystack mode. _$2.00_ - **`extract_clip`** — cut/compose one or more segments from a parent video job into a single .mp4 (with optional time-shifted transcript). _$0.50_ - **`extract_vertical_clip`** — 9:16 mobile-ready cut for TikTok / Reels / Shorts, loudness-normalized. _$0.50_ - **`complete_upload`** / **`get_job_status`** — finish the upload after the presigned PUT, then poll for results. *More coming soon* ## How it works 1. Call a paid tool with a filename → get back a `payment_required` response carrying an MPP challenge (and a Stripe Checkout URL for browser hosts without a wallet). 2. Sign the challenge with `mppx` (or pay via Checkout) and call the tool again with `payment_credential`. 3. Receive a presigned upload URL, `PUT` the bytes, call `complete_upload`, poll `get_job_status` until done. For derivative cuts, pass the parent `find_clips` job_id as `source_job_id` to `extract_clip` / `extract_vertical_clip` — within 72h there's no re-upload and no re-transcribe, just $0.50 per cut. ## Endpoints - MCP: `https://api.weftly.ai/mcp` (Streamable HTTP, JSON-RPC 2.0) - Web UI: `https://chat.weftly.ai` - Discovery: `/.well-known/mpp.json` · `/.well-known/openapi.json` · `/.well-known/cards/{tool}.md` Failed jobs auto-refund. Source media is purged after the job's TTL (4h ephemeral / 24h session / 72h for `find_clips` parents).
Connect through MCP
Attach the server under an Agent’s MCP servers configuration and inspect its advertised tools before use.
Production fit
SandBase keeps the capability alongside the model, runtime and execution trace so the workflow remains reviewable.

