Integration · AirOps
The managed workflow surface, agent-reachable both ways.
AirOps's first-party MCP server lives at app.airops.com/mcp with OAuth. A Claude Connector ships natively in Claude's Connectors Directory. Tools cover AEO data, brand visibility, competitor analysis, Brand Kits, and Knowledge Base search.
AirOps is itself an MCP-aware platform. Its workflow builder connects to many other MCP-enabled providers (Webflow, WordPress, Contentful, Notion, BigQuery, Snowflake, Semrush, Ahrefs, and more) inside a managed surface that handles auth, scheduling, retries, and human-in-the-loop review. The MCP and Claude Connector expose AirOps's own AEO (AI Engine Optimization) data and brand-visibility queries to the agent runtime. The result: an agent can query AirOps for brand visibility data, and AirOps can run multi-platform content workflows the agent triggered.
What you can do via MCP
Example prompts the agent runs.
“Query AirOps's AEO data for our brand's visibility across the answer engines this quarter; segment by content category.”
Calls the AEO-data tool, returns brand visibility metrics segmented by category (product, comparison, how-to, etc.). The cross-tab tells the team which content types are reaching the answer engines and which are not.
“Search the Knowledge Base for prior briefs we wrote on 'experimentation analytics'; list the workflows that produced them.”
Calls the Knowledge Base search tool, returns matching briefs and the AirOps workflow runs that generated them. The audit trail is the answer to 'how did we produce this last time and what changed since.'
“Update the Brand Kit's tone-of-voice section to match the latest brand-voice doc; show me the diff before saving.”
Reads the current Brand Kit, generates the updated tone-of-voice section from the new brand-voice doc, returns the diff for the human to confirm. Saves on confirm.
“Pull our top three competitors' citation share for the 'feature flag' topic; tell me which one is moving fastest.”
Calls the competitor-analysis tool, returns the top three competitors with their citation share and the period-over-period delta. The fastest mover is the team's prioritization signal for the next content investment.
“Trigger the AirOps workflow 'multi-channel article publish' for the brief I just approved; tell me which platforms it will hit.”
Reads the workflow's connected MCPs (Webflow, Contentful, WordPress, social channels), confirms the publish targets, returns the dry-run plan for human approval, then triggers the workflow on confirm.
AEO data query via the AirOps MCP. The agent specifies the topic and the time window; AirOps returns the visibility breakdown across answer engines.
# AirOps MCP, called via the agent runtime
mcp.airops.aeo_data({
brand_id: "acme",
topics: ["experimentation platforms", "feature flags"],
date_range: "last_90_days",
segment_by: ["content_category", "answer_engine"],
include_competitors: true
})
# Returns: brand_visibility_by_category, citation_share_by_engine,
# competitor_breakdown, share_of_voice_trend.MCP integration
AirOps MCP server.
- Server
- https://app.airops.com/mcp
- Auth
- OAuth (handled by AirOps)
- Hosting
- AirOps hosted (remote MCP at app.airops.com/mcp); Claude Connector available in the Connectors Directory
- Scoping note
- MCP scoping respects the OAuth user's role in the AirOps workspace. AirOps workflows that the agent triggers run inside the AirOps managed environment with the same permissions; downstream MCP connections (Webflow, Contentful, etc.) are scoped per the AirOps workspace's connected accounts.
- AEO (AI Engine Optimization) data: brand visibility across answer engines, queryable per topic
- Brand Kit management: tone of voice, style guide, brand-asset references
- Knowledge Base search: prior briefs, workflows, and outputs the team has produced
- Competitor analysis: citation share, share-of-voice deltas, content-category breakdowns
- AirOps as a meta-MCP: its workflows connect to Webflow, Contentful, WordPress, Notion, BigQuery, Snowflake, Semrush, Ahrefs, and more
- Claude Connector in the Connectors Directory plus the standalone MCP endpoint
Visual demonstration
What this looks like in practice.
AI engine optimization score
Vs Brand Kit voice and style
Recommended outline
- H1Feature Flag Rollout Strategy: A Practical Playbook
- H2What a feature flag rollout strategy actually decides
- H3Percentage rollout vs targeted cohort vs ring-deployment
- H3Kill switches, automatic rollbacks, and the on-call story
- H2Choosing rollout shape by feature riskinclude risk matrix
- H2Three rollout templates teams reuseship 3 examples
- H2Cleaning up after the rollout: stale flag hygiene
Entities to cover
- feature flag
- rollout strategy
- percentage rollout
- kill switch
- rollback
- Statsig
- LaunchDarkly
- GrowthBook
- stale flag
Brand mention rate
31.6%
+6.8%vs prior 30dShare of voice trend
Citation share by engine
- ChatGPT38.4%+2.6pp
- Perplexity30.2%+4.1pp
- Gemini19.8%+0.2pp
- Claude11.6%-0.1pp
Top-cited domains
- rampstack.cobrand246
- competitor-a.com198
- competitor-b.io142
- industry-blog.com116
- competitor-c.com92
- research.dev74
CLI alternative
AirOps REST API for non-MCP workflows.
AirOps exposes a REST API alongside the MCP for embedded analytics, scheduled exports to a warehouse, and CI-driven workflow triggers. The MCP and Claude Connector are the primary surfaces for AI-driven work inside the agent runtime; the REST API is the right shape for operational integrations the agent runtime is not built for.
Pairs with these skills
The content stack skill suite.
This integration pairs with the forthcoming content-strategy-for-ai-search and content-production skills. The AirOps microsite assumes the team has decided to run content production through a managed workflow surface; the integration shows where AirOps connects to the agent runtime in both directions. Skill pages and SKILL.md sources land in subsequent dispatches; cross-link hyperlinks are added when the skill pages ship.
On managed alternative vs build-it-yourself, honestly.
AirOps is a managed workflow alternative. The build-it-yourself path is to wire the agent runtime directly to the constituent MCPs (Webflow or Contentful for publishing, Frase for the SEO + GEO content lifecycle, Profound for visibility measurement, BigQuery or Snowflake for the data layer) and own the orchestration in the agent runtime. That path gives the team maximum control over how the workflow runs, where the prompts live, and how human review fits in. AirOps trades some of that control for managed auth, managed scheduling, managed retries, a workflow audit trail, and a human-review UI the team did not have to build. Neither shape is universally correct.
Pick AirOps when the team values the managed surface and does not want to own workflow orchestration in the agent runtime. Pick the build-it-yourself path when the workflow shape is already stable in the agent runtime, the team has the engineering capacity to maintain it, or the workflow needs custom logic AirOps does not yet express. The two are not mutually exclusive: AirOps is itself MCP-aware, so an agent can call AirOps for the workflows AirOps owns and call the constituent MCPs directly for everything else.