smithery.ai

activations

Query activation logs to check for errors and view volume

First seen Mar 28, 2026

Installation

$ npx skills add https://smithery.ai

Also in this package

Other skills from smithery.ai · top by installs.

npx skills add https://smithery.ai

Browse all from smithery.ai

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,754 B
  • docs SUMMARY.md 76 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

Activations

Treasue Data's real time services include digital marketing activations. These are sent to various destinations. This skill can be used to query the database table that stores activations logs for both successful and failed activations, which is a source of useful information for the digital marketer.

Description

Requirements

In order to query the activation log we must know the parent segment the customer is interested in. A customer may have a number of parent segments so we must ask them to provide the one they are interested before making a query. A segment ID will be a numeric value like 411671.

The user must also have a correctly configured Treasure Data mcp server to enable the database lookup @treasuredata/mcp-server

In addition the api key with appropriate access to the database table should be available and configured.

Database

The database name contains the parent segment ID and has this format cdpaudience394649_rt. This is where you can plug in the parent segment the user gives in the request.

Activations table

The activation log table is always called activations and is in the Parent Segment real time database.

Schema

The activations table has the following schema. The format is of the schema below is:

  1. Schema column index (example 1)
  2. Column name (example time)
  3. Query Column name (example time) (generally can be ignored for activations)
  4. Data type (string, time, int)

1 time time int 2 delivered delivered string -- 3 status status long -- 4 timestamp timestamp long -- 5 activationtype activationtype string -- 6 logtime logtime long -- 7 journeyname journeyname string -- 8 journeystagename journeystagename string -- 9 activationname activationname string -- 10 rid rid string -- 11 error error string -- 12 activationid activationid string -- 13 eventid eventid string -- 14 response response string --

For the purposes of making queries and talking about activations with the user here are the meanings of the columns.

time - epoch time of the activation send attempt delivered - true if successfully sent and false otherwise status - numeric status code corresponding to http response return codes timestamp activationtype - the text name of the activation. tdwebhookout for example logtime - journeyname - The real time journey this is related to. For example journey11738 journeystagename - The stage of the real time journey. For example journeystage15055. activationname - Customer's name for the activation. For example first activation14136 error - When an error occurs the text will be here. For example "AxiosError: Request failed with status code 404" activationid - Unique identifier of the activation. eventid - Unique identifier for the event which triggered the activation. response - Contains text that was returned from the activation server. This occurs whether or not the request succeeded. Example {"success":false,"error":{"message":"Token \"c58dd43b-5bbc-4d21-a81a-e8c5643bcc18\" not found","id":""}} It can be useful to show the response when errors are involved.

Query tips

If the user does not specify a time range assume the last 24 hours. Use a where clause like TD_INTERVAL(time, '-1d/now') When users are asking "What kind of errors are occuring" focus on unique error status and error texts so they can quickly identify issues. Provide summary data where appropriate. For example the customer may ask for "Which journeys are triggering the most activations", or "group the number of activations by journey". Use unique and other SQL constructs to make useful aggregations on the data.