Integrate the Mediatoolkit API with the Python API

Setup the Mediatoolkit API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Mediatoolkit and Python remarkably fast. Free for developers.

Popular Ways to Connect Mediatoolkit with Python#

Run Python Code with Python API on New Mention from Mediatoolkit API

Mediatoolkit + Python

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Popular Mediatoolkit and Python Triggers#

New Mention from the Mediatoolkit API

Emit new event on each new mention.

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Popular Mediatoolkit and Python Actions#

Run Python Code with the Python API

Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.

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Overview of Mediatoolkit#

Mediatoolkit is a powerful media monitoring tool that tracks online mentions of your brand or keywords across various platforms, helping you stay on top of public perception and industry trends. Using the Mediatoolkit API on Pipedream, you can automate reactions to these mentions, aggregate data for analytics, and integrate notifications into other apps to keep your team informed. This enables real-time media monitoring, sentiment analysis, and enhances your ability to respond swiftly to the social sentiments affecting your brand or market.

Connect Mediatoolkit#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    mediatoolkit: {
      type: "app",
      app: "mediatoolkit",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.mediatoolkit.com/me/organizations`,
      params: {
        access_token: `${this.mediatoolkit.$auth.access_token}`,
      },
    })
  },
})

Overview of Python#

Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.

This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.

Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:

Connect Python#

def handler(pd: "pipedream"):
  # Reference data from previous steps
  print(pd.steps["trigger"]["context"]["id"])
  # Return data for use in future steps
  return {"foo": {"test":True}}