Integration
Connect Feathery and Metaview
When Data Received fires in Feathery, your agent runs Send sourcing message in Metaview, or New Conversation in Metaview to Create or Fetch User in Feathery. 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 Metaview 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 and Metaview can do together
From Feathery to Metaview
When Data Received in Feathery
then Send sourcing message in Metaview
From Metaview to Feathery
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 Metaview
Triggers and actions 3 triggers · 42 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.
- New Conversation
Triggers when a new conversation is available in Metaview.
- Build chart
Creates a visual chart from conversation data with optional filtering, grouping, and timezone adjustment.
- Check enrichment status
Returns enrichment status and usage metrics for candidate data, with options to scope by person or enrichment type.
- Create AI field
Creates a new AI-powered field that analyzes conversations to answer a custom question, with configurable value type.
- Create report
Builds a report from conversations with filtering, metrics, grouping, and custom field inclusion.
- Fetch candidates
Looks up candidate information by ID, LinkedIn URL, email, or phone number in bulk.
- Find candidate's sequences
Finds which outreach sequences a candidate is enrolled in using their ID, LinkedIn URL, email, or phone.
- Get note template
Returns a note generation template by ID for use in automating note creation.
- Get search details
Retrieves configuration and results for a sourcing search by ID.
- Get sourcing analytics
Returns sourcing analytics and metrics grouped by user, search, or date range with filtering options.
- Get sourcing messages
Lists outreach messages sent in a sourcing search with pagination support.
- Get User Context
Returns information about the connected user account and available features.
- Give sourcing feedback
Submits feedback on candidates from a sourcing search, optionally linking to the search that sourced them.
- Group conversations
Groups recorded conversations by a field (like recruiter, stage, or outcome) and returns counts and metrics per group.
- List application reviews
Returns a list of application reviews, optionally filtering by name or showing only active ones.
- List ATS jobs
Returns ATS job postings, optionally filtered by job title.
- List ATS stages
Returns hiring pipeline stages for a job, or workspace-level stages if no job is specified.
- List available fields
Returns available data fields for reports, optionally filtered by name.
- List candidates in sequence
Returns candidates enrolled in a sequence, with optional enrollment status and per-step delivery details.
- List field values
Returns distinct values for a field across conversations, useful for building filters or dropdowns.
- List mailboxes
Returns email mailboxes available in this Metaview workspace.
- List note template folders
Returns folders of note templates organized in the workspace.
- List note templates
Returns note templates available in the workspace, optionally filtered by name or with full section details.
- List sequences
Returns candidate sequences, optionally filtered by name, creator, or status; includes full configuration if requested.
- List sourcing candidates
Returns candidates from a specified sourcing search with optional filtering by pack, feedback status, and detail level.
- List sourcing searches
Returns all sourcing searches with optional pagination.
- Manage candidate sequence
Adds, pauses, resumes, cancels, removes, or updates candidate enrollments in a recruitment sequence.
- Manage note template
Creates, updates, or deletes call note templates with customizable sections for structured note-taking.
- Manage sequence
Creates, updates, duplicates, or deletes recruitment sequences.
- Search conversations
Searches call conversations with optional filtering, sorting, and field selection.
- Search reports
Searches reports by name, category, or ID with optional full configuration details.
- Send sourcing message
Starts or continues a sourcing or research search with a message to the AI agent.
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 Metaview, 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 Metaview when something happens in Feathery?
Any of Metaview's 31 actions, including Create AI field, Create report, Send sourcing message and Build chart. Your agent maps the data both ways and tests every scenario before it ships.
Can it also run from Metaview to Feathery?
Yes. When something happens in Metaview, your agent can act in Feathery too. This page covers both directions: Metaview's triggers and Feathery's actions are listed here as well.
Do I have to build a connector for Feathery or Metaview?
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 Metaview 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.