Why this subject deserves an implementation guide
The starting question for this article came from a public Context.dev post titled "Monkeflow Generates On-Brand Marketing Emails Instantly with Context.dev". Rather than reproduce that article, this guide asks what the same product problem looks like inside MediaHarvester and what can be verified in the running application.
Growth teams need evidence that makes a decision sharper, not a pile of scraped facts presented as strategy.
The MediaHarvester approach
Define the segment or hypothesis first, gather public company and positioning context, record supporting pages, then use the structured result in a human-reviewed experiment.
The primary surface for this workflow is `POST /v1/brand/ai/query`. It can be tried from the API playground and integrated through the local API key, SDK, CLI or MCP layer.
Workflow map
A realistic application scenario
A product marketer is generating a competitive brief and needs current feature and positioning facts with sources.
The workflow begins with a narrowly scoped public or authorized source, records the endpoint output and makes the result reviewable before it becomes visible to users or informs an automated decision.
Try the capability
POST /v1/brand/ai/query Local API key:mh-localhost-dev-key
POST /v1/brand/ai/queryImplementation choices that matter
Public web context can improve account research and competitive understanding; it should not be used to infer private behavior or justify spam.
This matters because a production feature is judged less by a perfect demo result than by how it behaves when an asset is missing, a source changes, a response is cached or a request is not allowed.
How to measure whether it works
Track analyst time, evidence completeness, correction rate, qualified responses and experiment performance against an unenriched baseline.
The app should retain enough source and request metadata to debug poor results while applying appropriate retention and access policies for customer data.
A responsible next step
Run the included endpoint against a website you control or are authorized to process, inspect the response in Visual and JSON modes, then decide which fields deserve automation and which deserve human approval.
MediaHarvester deliberately treats blocked, verification-required, login-required, robots-disallowed and permission-required outcomes as information, not obstacles to be bypassed.
FAQ
Questions teams ask before implementing this workflow
What does this applied ai workflow return?
It uses POST /v1/brand/ai/query and related MediaHarvester surfaces to return structured context together with metadata appropriate to the workflow.
Can I test this locally?
Yes. Run the local service at http://127.0.0.1:8013 and send X-API-Key: mh-localhost-dev-key to protected API routes.
Does it work with private or blocked pages?
The platform is designed for publicly accessible or authorized sources. Verification, login, permission and robots restrictions are reported rather than bypassed.
Can this be automated?
The same API surfaces are available through CLI, Python and TypeScript SDKs, MCP tools and starter no-code integration templates.
How do I keep the result current?
Use cache freshness controls such as maxAgeMs where exposed, and schedule refreshes in a production worker only as frequently as the business case needs.