Integration
Connect Feathery to Descript
When Data Received fires in Feathery, your agent runs Export project transcript in Descript. Describe the whole flow and it is built, tested on every scenario and kept running.
What the catalog holds today. Ask for anything missing and your agent reads the API documentation and adds it.
Build solutions, not workarounds.
What do you want Feathery and Descript to do together?
Ask the assistant
Connect your AI client
Give your AI client this address.
https://mcp.apiant.aiOne connection does both: ask your client to act in an app once, or have it build an automation that keeps running. Any client that takes a remote MCP server. Nothing to install. Create your free account, then sign in once in your browser.
Set it up in your client
Claude Code
Run this in the directory you want to work in.
claude mcp add --transport http -s project apiant-ai https://mcp.apiant.aiStart Claude Code there, approve the server, and sign in in the browser page it opens.
Claude Code guide (opens in a new tab)Claude, web and desktop
Open Customize, then Connectors, then Add custom connector, and paste the address. On Team and Enterprise an owner adds it first.
Setup guide (opens in a new tab)Cursor
Add the address to your MCP configuration as a server URL. Cursor signs you in in your browser.
Setup guide (opens in a new tab)Zed
Open Settings, then AI, then MCP Servers, then Add Server, then Add Remote Server.
Setup guide (opens in a new tab)Codex and the ChatGPT desktop app
Open Settings, then MCP servers, then Add server, and choose Streamable HTTP. From the terminal, Codex takes the address with its own add command and then signs you in.
Setup guide (opens in a new tab)Any other MCP client
Paste the address where the client asks for a remote MCP server URL, and name the server apiant-ai where it lets you. APIANT's skills reach the client over the same connection.
Setup guide (opens in a new tab)Example flows
What Feathery can start in Descript
When Data Received in Feathery
then Export project transcript in Descript
Starting points drawn from the triggers and actions in the catalog today. Describe the flow you need, at any depth, and your agent builds it and tests every scenario before it ships.
Everything your agent can do with Feathery and Descript
Triggers and actions 2 triggers · 19 actions
- Data Received
Triggers when an end user submits or updates data on a Feathery form.
- Form Completion
Triggers when an end user completes a Feathery form.
- Create Hidden Field
Creates a hidden field in your Feathery account.
- Create or Fetch User
Creates a Feathery end user with the given ID, or returns the existing user, including its SDK key.
- Create or Update Submission
Sets field values for a Feathery user and initializes submissions on the given forms.
- Delete User
Deletes a Feathery end user and their submissions.
- Get User Field Data
Returns every field value a Feathery user submitted across forms.
- Get User Form Session
Returns a Feathery user's form session: every form they are working on and their progress.
- List Document Templates
Returns document templates uploaded by your Feathery team, optionally filtered by name or tags.
- List Forms
Returns your Feathery forms, optionally filtered by tags or active status.
- List Form Submissions
Returns submissions for a Feathery form, optionally filtered by user, completion and update time.
- List Hidden Fields
Returns all hidden fields in your Feathery account.
- List Users
Returns your Feathery end users, optionally filtered by creation time or a field value.
- Cancel job
Cancel a pending/running async job by job_id.
- Export project transcript
Export the transcript of a Descript project composition as plain text, Markdown, HTML, RTF, DOCX or SRT subtitles. The transcript comes back in the response itself.
- Get project
Get details of a single Descript project by project_id.
- Get published project metadata
Partner API: get metadata (title, duration, publisher, privacy, subtitles) for a published Descript project by its URL slug.
- List agent models
List the Underlord agent models available for use with the Agent edit operation.
- List projects
List Descript projects on the account.
- Publish project media
Publish a Descript project's media (render + publish output).
- Run agent edit
Prompt the Underlord AI agent to perform edits on a Descript project (e.g. remove filler words, generate clips).
No trigger or action matches that. Ask your agent to add it: it reads the API documentation and builds what you describe.
Each app on its own
Reviews
What our customers say
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Questions
Feathery and Descript, answered
What can start an automation from Feathery?
Any of Feathery's 2 triggers in the catalog, including Data Received and Form Completion. Describe the moment in plain language and your agent picks the right one.
What can your agent do in Descript when something happens in Feathery?
Any of Descript's 8 actions, including Export project transcript, Get project, Get published project metadata and List agent models. Your agent maps the data both ways and tests every scenario before it ships.
Can it also run from Descript to Feathery?
Descript has no triggers in the catalog today, so automations start in Feathery. If you need the other direction, ask: your agent reads Descript's API documentation and adds it.
Do I have to build a connector for Feathery or Descript?
No. Both are in the catalog with their triggers and actions. You describe the automation; your agent builds it, tests every scenario and keeps it running.
Can my AI client use Feathery and Descript without building an automation?
Yes. With your AI client connected to APIANT, ask it to do something in either app once and it runs that action on the spot. Ask for an automation and your agent builds it, tests every scenario and keeps it running. One connection covers both.
What happens when something fails?
Your agent works that out for you. It plans the failure paths along with the happy path, then tests every scenario before it ships.
Is there a free plan?
Yes. Every plan includes the agent, including free, and building from your own AI client spends no assistant credits.