Mercury Glue

Create new Glue threads for every new transaction in Mercury.

Whenever a new transaction takes place in Mercury, create a new thread in Glue.

Try:

See all Mercury + Glue integrations or talk to a human

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How Knit builds this workflow

The Agent researches both APIs and wires the trigger on one side to the action on the other — no pre-built connector required. Here is the vocabulary it has to work with.

Mercury Explore Mercury →

Triggers
New expense submitted

Fires when an employee submits a new expense in Mercury.

Expense approved

Fires when an expense in Mercury is approved.

Expense rejected

Fires when an expense in Mercury is rejected.

Receipt uploaded

Fires when a receipt is attached to an expense in Mercury.

Actions
Create expense report

Creates a new expense report in Mercury.

Approve expense

Approves a pending expense in Mercury.

Reject expense

Rejects a pending expense in Mercury.

Sync to accounting

Syncs an approved expense in Mercury to the general ledger.

Glue Explore Glue →

Triggers
New message posted to a channel

Fires when a new message is posted to a channel in Glue.

Mentioned in a message

Fires when your team or a keyword is mentioned in Glue.

New channel created

Fires when a new channel is created in Glue.

New file shared

Fires when a new file is shared in Glue.

Actions
Send a channel message

Posts a message to a channel in Glue.

Send a direct message

Sends a direct message to a person in Glue.

Create a channel

Creates a new channel in Glue.

Invite a user to a channel

Adds a user to a channel in Glue.

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FAQ

Common questions

Can Knit build “Create new Glue threads for every new transaction in Mercury.” between Mercury and Glue?

Yes — describe it in the box above and Knit's Integrations Agent researches Mercury and Glue's public API docs (or your own uploaded docs) and builds a working workflow, whether or not either app already has a pre-built connector.

How long does it take to build?

Minutes to a first working version, not weeks — you test it against real data before it goes anywhere near production.

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