> ## Documentation Index > Fetch the complete documentation index at: https://docs.rocketblue.ai/llms.txt > Use this file to discover all available pages before exploring further. # MCP use cases > Practical MCP workflows for automating AI visibility monitoring, content, and prompt ops. Practical ways to automate AI visibility work with rocketblue MCP — digests, competitor monitoring, prompt hygiene, content pipelines, and more. Connect an assistant once, then ask it to run these workflows in natural language. Each use case lists the MCP tools involved so you can adapt the flow in Cursor, Claude Desktop, or any MCP client. ## Weekly visibility digest **Goal:** After each scheduled report, produce a short summary for Slack, email, or a Notion page. **Ask the agent:** ```text theme={null} For brand BRAND_ID, get the latest run stats and top mentioned competitors. Write a 5-bullet weekly digest: share of voice, biggest gains/losses vs prior run, and one recommended action. ``` **Tools:** `list_brand_runs` → `get_run_stats` (current + previous run) → optional `get_brand_last_results` ## Competitor share-of-voice watch **Goal:** Flag when a competitor jumps in mention share on priority prompts or topics. **Ask the agent:** ```text theme={null} Compare the last 4 runs for brand BRAND_ID. Highlight any competitor whose topic-level mention share rose more than 5 points. List the prompts where they appear. ``` **Tools:** `list_brand_runs` → `get_run_stats` → `get_run_results` (only for flagged prompts) ## Prompt portfolio hygiene **Goal:** Keep the tracked prompt set aligned with real buyer questions and within quota. **Ask the agent:** ```text theme={null} Check remaining prompt slots for brand BRAND_ID. Find prompts with zero brand mentions across the latest run. Propose replacements that are neutral and consideration-stage, then wait for my approval before deleting or creating. ``` **Tools:** `get_brand_context` → `get_usage_guide` → `get_brand_last_results` → `delete_prompts` / `batch_create_topics_and_prompts` ## Citation gap and source map **Goal:** See which domains LLMs cite for your category — and where you are missing. **Ask the agent:** ```text theme={null} For the latest run of brand BRAND_ID, summarize cited domains from get_run_sources. Call out domains that often cite competitors but not us, and suggest 3 outreach or content angles. ``` **Tools:** `list_brand_runs` → `get_run_sources` → `get_run_stats` ## AEO keyword mining from model search queries **Goal:** Turn the queries models actually searched into a content and SEO backlog. **Ask the agent:** ```text theme={null} Pull web search queries for brand BRAND_ID (ChatGPT and Perplexity only). Cluster them into themes and output a prioritized brief list of article titles. ``` **Tools:** `get_brand_web_search_queries` (with `llmNames`) → optionally `get_run_content_suggestions` ## Content pipeline from suggestions to drafts **Goal:** Move from run suggestions into drafted articles your team can publish. **Ask the agent:** ```text theme={null} List content suggestions from the latest run for brand BRAND_ID. Pick the top 3 by opportunity, then list any matching created-content drafts we already have. If a draft exists, open it and summarize readiness to publish. ``` **Tools:** `list_brand_runs` → `get_run_content_suggestions` → `list_brand_created_content` → `get_created_content` ## Publish-ready draft export **Goal:** Hand CMS/docs a full markdown draft with schema hints. **Ask the agent:** ```text theme={null} Fetch created content CONTENT_ID for brand BRAND_ID. Return the markdown body, excerpt, suggested schema types, and research sources as a single publish package. ``` **Tools:** `get_created_content` ## Perception score tracking **Goal:** Monitor branded perception properties (trust, clarity, etc.) over time. **Ask the agent:** ```text theme={null} Export perception prompt responses for brand BRAND_ID for the latest run and the run before it. Table the score deltas and quote one representative LLM explanation per property that dropped. ``` **Tools:** `list_brand_runs` → `get_run_perception_prompts_responses` (×2) ## Agency multi-brand rollup **Goal:** One assistant pass across every brand in the org for leadership reporting. **Ask the agent:** ```text theme={null} List all brands. For each, pull brand context and latest run stats. Produce a table: brand, plan slots remaining, last run date, main-brand mention rate, top competitor. Flag brands with no run in 14+ days. ``` **Tools:** `get_organization_context` → `list_brands` → `get_brand_context` + `list_brand_runs` + `get_run_stats` per brand ## Seed prompts from a research brief **Goal:** Turn a GTM or category brief into a tracked topic/prompt set before the next report. **Ask the agent:** ```text theme={null} Read this brief: [...]. Using get_usage_guide rules, propose topics and neutral prompts for brand BRAND_ID. Check quota first. After I approve, create them. ``` **Tools:** `get_usage_guide` → `get_brand_context` → `batch_create_topics_and_prompts` ## Tips for reliable automation * Always start with `get_organization_context` and `get_brand_context` so the agent knows plan limits and schedule. * Prefer `get_run_stats` for dashboards; use `includeAnswer: true` only when you need raw model text. * Writes land on the **next scheduled report** — digests should wait for a completed run, not fire immediately after prompt edits. * For bulk prompt cleanup, use `delete_prompts` once with all IDs. * Viewer/guest roles are read-only; write tools will fail for those accounts. ## Related Wire up Cursor or Claude Desktop. Full tool catalog and REST mappings. Quotas, prompt style, and session flow. Same data over HTTP for scripts and backends.