Connect Facebook Public Data to ChatGPT

Multi-Client Page Health Watchlist

Using the Catchr MCP, pull Facebook Public Data for [List of Client Page Names/IDs] over [Recent Period] and compare it with [Baseline Period]. For each Page Name, report Post Count, Follower Count, Fan Count, Talking About Count, Reactions Per Post, Comments Per Post, and Shares Per Post; use the latest available page-level count rather than summing repeated snapshots. Apply [Client-Specific Targets], label each page Healthy, Watch, or Critical, rank the accounts the consultant should review first, and give one evidence-based diagnosis and one concrete content or community-management action for every flagged client.

Client-versus-Competitor Content Benchmark

Using the Catchr MCP, compare [Client Page Name/ID] with [Competitor Page Names/IDs] over [Benchmark Period]. Use Page Name, Page Category, Post Type, Post Created At, Post Message, Post Title, Post Count, Reactions Count, Comments Count, Shares Count, and the latest Follower Count to benchmark publishing cadence, interaction volume, interactions per post, and interactions per follower. Identify the content types and inferred themes that outperform within each page and across the peer set, clearly label themes as inferred from public post text, and deliver a client-ready scorecard with three specific opportunities, supporting posts via Post Permalink Url, and the KPI to monitor for each recommendation.

Monthly Public Content Client Brief

Using the Catchr MCP, analyze Facebook Public Data for [Client Page Name/ID] during [Reporting Period] versus [Comparison Period]. Summarize Post Count, Reactions Per Post, Comments Per Post, Shares Per Post, Talking About Count, Follower Count, and the reaction mix from Likes Count, Reactions Love, Reactions Wow, Reactions Haha, Reactions Sad, and Reactions Angry. Use Post Message, Post Title, Post Description, Post Type, and Post Permalink Url to explain which public content themes and formats gained or lost interaction, marking all theme interpretations as inferred. Write a concise client-ready report with wins, risks, five example posts, and three prioritized actions for [Next Period], without claiming that public engagement caused sales or conversions.

Daily Client Content Priority Queue

Using the Catchr MCP, pull Facebook Public Data for [List of Client Page Names/IDs] over [Recent Period, e.g., the last 7 days] and compare it with [Baseline Period, e.g., the preceding 28 days]. For every Page Name, evaluate posting gaps from Post Created At and Post Count, plus changes in Reactions Per Post, Comments Per Post, Shares Per Post, Talking About Count, and the latest Follower Count. Apply [Client-Specific Cadence and Engagement Targets], rank the clients I should address today, and provide the affected Post Permalink Url, the measurable reason for the priority, and one exact first action for each page.

Monthly Public Page Performance Story

Using the Catchr MCP, create a client-ready Facebook Public Data report for [Client Page Name/ID] covering [Current Period] versus [Previous Period]. Summarize Post Count, Reactions Count, Comments Count, Shares Count, Reactions Per Post, Comments Per Post, Shares Per Post, Talking About Count, and the latest Follower Count and Fan Count. Use Post Type, Post Message, Post Title, Post Description, and Post Permalink Url to show the posts and inferred themes behind the main changes. Write the report in plain English with what improved, what declined, what the public data cannot prove, and three priorities for [Next Period], each tied to a measurable public-engagement KPI.

Next-Month Content Brief Builder

Using the Catchr MCP, analyze [Client Page Name/ID] and [Competitor Page Names/IDs] over [Research Period] to build the client's content brief for [Next Month]. Rank posts using Reactions Count, Comments Count, and Shares Count while also showing interactions per post and the latest Follower Count for context. Infer recurring themes, hooks, formats, and calls to action from Post Message, Post Title, Post Description, Post Type, and Post Link, label those interpretations as inferred, and cite examples with Post Permalink Url. Produce [Number of Concepts] original content concepts with objective, angle, suggested format, evidence from the benchmark, publishing slot, and the metric that will determine whether each concept worked; do not copy competitor wording.

Product and Offer Content Winner Finder

Using the Catchr MCP, pull Facebook Public Data for [Brand Page Name/ID] over [Period]. Infer product, collection, promotion, and offer themes from Post Message, Post Title, Post Description, and Post Link, and group results by those inferred themes and Post Type. For each group, report Post Count, Reactions Count, Comments Count, Shares Count, Reactions Per Post, Comments Per Post, and Shares Per Post, then identify the strongest and weakest posts with Post Permalink Url. Recommend which three content angles to repeat, revise, or stop and define a public-engagement KPI for each next test; treat interactions as content signals only and do not infer revenue, clicks, or conversions.

Launch and Reputation Reaction Pulse

Using the Catchr MCP, analyze Facebook Public Data for [Brand Page Name/ID] from [Pre-Launch Start Date] through [Post-Launch End Date], split around [Launch Date]. Filter relevant launch posts using [Product or Campaign Keywords] in Post Message, Post Title, or Post Description, then compare Post Count, Reactions Count, Comments Count, Shares Count, and the mix of Likes Count, Reactions Love, Reactions Care, Reactions Wow, Reactions Haha, Reactions Sad, and Reactions Angry before and after launch. Flag posts with unusual negative-reaction shares or comment-volume spikes against [Baseline Threshold], link each finding with Post Permalink Url, and produce a response and content-adjustment plan while treating reaction patterns as signals rather than proof of customer sentiment or product quality.

Publishing Cadence and Timing Optimizer

Using the Catchr MCP, pull Facebook Public Data for [Brand Page Name/ID] over [Lookback Period, e.g., the last 90 days] and analyze performance by Day of the week, Hour of the day, Post Type, and Post Created At. Compare Post Count, Reactions Per Post, Comments Per Post, and Shares Per Post across posting windows, requiring [Minimum Posts per Segment] before ranking a time slot. Separate timing effects from content-mix differences by showing the dominant Post Type and inferred Post Message theme in each segment, then recommend a four-week publishing schedule with time slots, formats, test hypotheses, and success thresholds; state clearly where the sample is too small for a reliable conclusion.

Public Post Data Integrity Audit

Using the Catchr MCP, audit Facebook Public Data for [Page Name/ID or All Available Pages] over [Period]. Check completeness and uniqueness across Page Id, Page Name, Post Id, Post Created At, Post Updated At, Post Message, Post Type, Platform Name, and Extracted Date; also flag negative values in Post Count, Reactions Count, Likes Count, Comments Count, Shares Count, Follower Count, Fan Count, or Talking About Count. Test whether Reactions Count plausibly reconciles with Likes Count plus the available reaction subtype fields, treating mismatches as suspicious rather than definitive because reaction categories may be incomplete. Return a reproducible exception table with Page Id, Post Id, failed rule, observed value, severity, certainty, and the next pipeline validation step.

Extraction Freshness and Change Monitor

Using the Catchr MCP, monitor Facebook Public Data for [Page Name/ID or All Available Pages] over [Lookback Period] using Extracted Date, Post Created At, Post Updated At, Page Id, Post Id, Post Count, Follower Count, Fan Count, and Talking About Count. By page and extraction date, flag extraction lag above [Maximum Lag], missing extraction dates, unexpected gaps in newly observed posts, duplicate Post Id records within the same snapshot, conflicting immutable post attributes, and abrupt page-count changes above [Change Threshold]. Distinguish definite pipeline failures from possible real page activity or post edits, and output a page-level health summary plus a record-level investigation queue with the exact follow-up check for each anomaly.

Cross-Page Benchmark Dataset Builder

Using the Catchr MCP, build an analysis-ready Facebook Public Data benchmark for [List of Page Names/IDs or Page Category] over [Period]. At Page Id, Date, and Post Type grain, calculate Post Count, Reactions Count, Comments Count, Shares Count, Reactions Per Post, Comments Per Post, Shares Per Post, posting cadence, and interactions per follower using the latest non-missing Follower Count for the relevant snapshot; never average page-level ratios when they can be recomputed from totals. Profile missingness, distinguish zero from unavailable values, apply [Minimum Post and Follower Thresholds], detect outliers against comparable Page Category groups, and return a documented benchmark table, data-quality report, and definitions for every derived metric so the dataset can feed a dashboard or model.

Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Connect Facebook Public Data to ChatGPT

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How to connect Facebook Public Data to ChatGPT ?

Authorize the Facebook Public Data account in Catchr

Add the Catchr MCP from official ChatGPT store

Choose, adapt, and run a prompt.

A simple client question should not trigger an hour of data prep.

Pull another CSV

Rebuild the context

Summarize it by hand

Ask the account. Get to the explanation.

Prepare data before every question.

Connect once. Keep asking.

Twelve Facebook Public Data prompts for analyzing real account data.

Facebook Public Data to ChatGPT FAQs

What Facebook Public Data data can ChatGPT analyze through Catchr?

How granular can Facebook Public Data analysis be in ChatGPT?

Can ChatGPT identify the content that performs best in Facebook Public Data?

Can ChatGPT analyze audience growth and publishing patterns in Facebook Public Data?

What types of prompts work best for Facebook Public Data analysis?

Can I combine Facebook Public Data with other data sources in ChatGPT?

Can I analyze multiple Facebook pages together?

What do I need to connect Facebook Public Data to ChatGPT with Catchr?

How recent is the Facebook Public Data data, and how much history can I analyze?

Can ChatGPT change anything in Facebook Public Data through Catchr?

How much does it cost to connect marketing data to ChatGPT with Catchr?

‍Can I try the ChatGPT integration before choosing a plan?

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