<h2 id="zq1m808op2r" data-toc-id="zq1m808op2r">1. Visibility rate</h2><details class="details"><summary>Brand Visibility Timeline by LLM</summary><div data-type="detailsContent">Create a line graph for the visibility of tracked term [BRAND NAME] in project [PROJECT NAME] from [DATE] to [DATE]. Show this per LLM active during this period so we can easily compare performance per LLM.</div></details><details class="details"><summary>Brand Visibility Heatmap</summary><div data-type="detailsContent">For project [PROJECT NAME], render a heatmap where the X-axis shows LLMs and the Y-axis shows brand terms, sorted by overall visibility descending. Color cells on a single-ramp scale (light → dark = low → high visibility %), and show the percentage value inside each cell. Goal: instantly see which brand dominates which LLM.</div></details><details class="details"><summary>Brand Visibility Heatmap Filtered by Tag</summary><div data-type="detailsContent">For project [PROJECT NAME], render a heatmap where the X-axis shows LLMs and the Y-axis shows brand terms, sorted by overall visibility descending. Color cells on a single-ramp scale (light → dark = low → high visibility %), and show the percentage value inside each cell. Goal: instantly see which brand dominates which LLM. Do this for the tag ‘TAG NAME’.</div></details><h2 id="zzwxiowdywx" data-toc-id="zzwxiowdywx">2. Citation rate</h2><details class="details"><summary>Brand Citation Rate Timeline by LLM</summary><div data-type="detailsContent">Create a line graph for the citation rate of tracked term [BRAND NAME] in project [PROJECT NAME] from [DATE] to [DATE]. Show this per LLM active during this period so we can easily compare performance per LLM.</div></details><details class="details"><summary>Citation Momentum Chart</summary><div data-type="detailsContent">Create a line graph for the citation rate of tracked term [BRAND NAME] in project [PROJECT NAME]. Show results for ChatGPT and overlay a 30-day moving average to highlight trend momentum and reduce noise.</div></details><details class="details"><summary>Citation Gap Tracker</summary><div data-type="detailsContent">Create a line graph showing the difference between tracked term [BRAND NAME] and [BRAND NAME] citation rate for ChatGPT over time. Goal: measure distance to the leader. Do this for the tag ‘TAG NAME’.</div></details><details class="details"><summary>Small Multiples View</summary><div data-type="detailsContent">Create separate mini line charts for each active LLM using the same Y-axis scale. Goal: make pattern comparison easier without overlapping lines. Do this for tracked terms [BRAND NAME] and [BRAND NAME].</div></details><h2 id="2iewk8zff8l" data-toc-id="2iewk8zff8l">3. Gap Analysis</h2><details class="details"><summary>Missing Visibility Opportunities</summary><div data-type="detailsContent">Pull all prompts in the project [PROJECT NAME] with the tag [TAG NAME] where the tracked term [BRAND NAME] has a visibility score of 0% (meaning [BRAND NAME] was not mentioned in the response).For each of these prompts, return:- The prompt text - [BRAND NAME] visibility % - Visibility % for [COMPETITOR 1] - Visibility % for [COMPETITOR 2]Sort the results by the highest competitor visibility score, so I can quickly identify prompts where competitors are performing well while [BRAND NAME] is absent. </div></details><details class="details"><summary>Content Gap Discovery Report</summary><div data-type="detailsContent">Identify all prompts in project [PROJECT NAME] where my average visibility score was below 10% over the last 14 days.For each identified prompt:- Retrieve the top 5 most frequently cited URLs associated with that prompt during the same period. - Analyze these URLs and identify the content type (blog article, comparison page, product page, guide, FAQ, landing page, directory, research page, etc.) - Based on this analysis, recommend the single best content asset I should create to increase my chances of being cited in AI responses.For each prompt, provide:- Prompt text - Current average visibility % - Top 5 cited URLs - Page title per URL</div></details>

MCP Prompt Library

Rankshift

Getting started

Getting started with Rankshift

How to Create a New Project in Rankshift

Understanding your metrics

Prompts

Introduction to prompts

Create prompts

Activate prompts (via scenarios)

Tagging your prompts

Finding the right prompts

Sources

Understanding sources

How to Get Your Brand Cited as a Source

Brands & Competitors

Add Competitors

Monitoring

Rankshift automatically analyzes the sentiment of AI responses to your prompts. Each response receives a sentiment label — positive, neutral, or negative — so you can instantly see how AI models are speaking about your brand or topic.

Sentiment

Brand Perception maps out which themes and associations AI models link to your brand. This way, you know not only whether people speak positively or negatively about you, but also exactly what they're saying (and per AI model).

Brand Perception

Connect your data with BI solutions like Looker Studio, Tableau, Power BI, and many more

Integrations 

Google Looker Studio

Query Fanouts

What are Query Fanouts 

Where to find Query Fanouts in Rankshift?

How to use Query Fanouts

Connect to our MCP to review or leverage your Rankshift data in ChatGPT, Claude or other tools.

The Rankshift MCP connects your AI assistant to Rankshift analytics. 

MCP Server [OVERVIEW]

Step-by-step setup to connect the Rankshift MCP server to Claude and ChatGPT, using OAuth in 5 minutes.