Connect X / Twitter Public Data to Claude

Multi-Client X / Twitter Public Data Health Radar

Using the Catchr MCP, analyze X / Twitter Public Data for [List of Client Accounts] over [Reporting Period] against [Comparison Period]. For each account, review Impression Count, Like Count, and Followers Count and break the results down by Tweet Created At and Tweet Text. Focus on content formats and themes that earn meaningful engagement. Rank accounts as Healthy, Watch, or Critical using [Client Targets or Historical Baseline], and show the evidence behind each status. Return a client-ready table with the account, main change, supporting fields, business implication, recommended next action, and one sentence for the next client meeting. Separate observed changes from possible explanations, respect the reporting grain, and do not claim causation when X / Twitter Public Data only shows correlation.

Client-Ready X / Twitter Public Data Performance Brief

Using the Catchr MCP, review X / Twitter Public Data for [Client Account] during [Reporting Period] versus [Comparison Period]. Review Followers Count, Reply Count, and Retweet Count and break the results down by Tweet Language and Account Username. Explain what improved, what declined, and which changes are material for content, audience, reach, engagement, and profile performance. Produce an executive summary, three evidence-backed wins, three risks, and a prioritized action plan with owner and review date. Include a compact evidence table that ties every conclusion to the returned fields. State any unavailable field, incompatible reporting level, or small-sample limitation before recommending action, and write the final recap in language an agency account manager can use with the client.

X / Twitter Public Data Opportunity and Risk Map

Using the Catchr MCP, compare X / Twitter Public Data across [List of Client Accounts] for [Period] using [Materiality Threshold] and [Client-Specific Targets]. For every account, review Retweet Count, Quote Count, and Bookmark Count and break the results down by Account Name and Tweet Id. Investigate audience growth, reach quality, and profile actions and posting patterns, distribution gaps, and repeatable content opportunities. Build an opportunity-and-risk map ranked by business relevance, confidence, and urgency. For each finding, show the account, observed change, supporting fields, plausible explanations, first validation step, and recommended client action. Keep each client separate before creating a portfolio summary, and avoid applying one account's baseline, currency, attribution rule, or reporting definition to another.

Morning X / Twitter Public Data Priority Queue

Using the Catchr MCP, pull X / Twitter Public Data for [List of Client Accounts] over [Recent Period] and compare it with [Baseline Period]. For each client, review Bookmark Count, Tweet Count, and Following Count and break the results down by Tweet Author Id and Date. Flag changes beyond [Alert Threshold], separate high-value issues from low-volume noise, and rank the work I should handle today. Return a queue with the client, issue, exact evidence, likely impact stated cautiously, first validation step, recommended action, and a short update I can send to the client. Do not merge accounts until each result has been checked against its own target, currency, timezone, and reporting grain.

Monthly X / Twitter Public Data Client Report

Using the Catchr MCP, create a monthly X / Twitter Public Data report for [Client Account] covering [Current Period] versus [Previous Period]. Review Following Count, Listed Count, and Impression Count and break the results down by Account Most Recent Tweet Id and Account Pinned Tweet Id. Explain the most important movement in content, audience, reach, engagement, and profile performance, including what stayed stable and what needs attention. Write a plain-English report with an executive summary, a concise evidence table, three wins, three concerns, and three actions for [Next Period]. Attach an owner and success measure to each action. State the latest available date and any missing fields or intervals so the client can distinguish measured facts from assumptions or incomplete coverage.

X / Twitter Public Data Optimization Backlog

Using the Catchr MCP, analyze X / Twitter Public Data for [Client Account] over [Lookback Period]. Review Impression Count, Like Count, and Followers Count and break the results down by Account Country Codes and Tweet Country Codes. Identify the largest opportunities related to content formats and themes that earn meaningful engagement and posting patterns, distribution gaps, and repeatable content opportunities, then create an optimization backlog ranked by expected impact, effort, and confidence. For every task, include the observed evidence, a specific action, an owner, a due date, a success metric, and a guardrail that would stop the test. Use [Minimum Volume] and [Materiality Threshold] to avoid reacting to noise, and label any recommendation that requires a field or business definition not available in the returned dataset.

X / Twitter Public Data Content Format Performance Review

Using the Catchr MCP, analyze X / Twitter Public Data for [Account or Entity] over [Reporting Period] versus [Comparison Period]. Review Followers Count, Reply Count, and Retweet Count and break the results down by Account Created At and Account Description. Concentrate on content formats and themes that earn meaningful engagement. Identify the entities and periods driving the largest change, distinguish material movement from normal variation using [Minimum Volume] and [Business Target], and explain what the available fields can and cannot prove. Return a ranked findings table with evidence, business implication, confidence, and recommended next action, followed by a short decision summary. Keep calculations at a compatible reporting grain and do not invent a metric when X / Twitter Public Data exposes only a related signal.

X / Twitter Public Data Audience Growth and Engagement Map

Using the Catchr MCP, investigate X / Twitter Public Data for [Account or Entity] across [Period] with a focus on audience growth, reach quality, and profile actions. Review Retweet Count, Quote Count, and Bookmark Count and break the results down by Account Id and Account Location. Compare relevant entities against [Target, Peer Group, or Historical Baseline], then locate the strongest opportunities and clearest deterioration. For every finding, show the returned values, comparison dimension, possible explanations, and next check required before action. Deliver a prioritized opportunity map with owner, action, expected signal of improvement, and review date. Treat associations as hypotheses unless the available X / Twitter Public Data fields establish a direct relationship.

X / Twitter Public Data Publishing Pattern Opportunity Scan

Using the Catchr MCP, review X / Twitter Public Data for [Account or Entity] over [Lookback Period] to assess posting patterns, distribution gaps, and repeatable content opportunities. Review Bookmark Count, Tweet Count, and Following Count and break the results down by Account Profile Image Url and Account Protected. Flag changes outside [Tolerance], rank them by materiality and confidence, and separate confirmed data observations from possible operational causes. Return an exception table with the affected entity, period, supporting fields, severity, owner, first validation step, and recommended response. End with three repeatable monitoring rules, including the threshold, minimum volume, and required field. Do not present an unusual value as proof of an error or business cause.

X / Twitter Public Data Data Quality Audit

Using the Catchr MCP, audit X / Twitter Public Data for [Account or All Connected Accounts] over [Period]. Review Following Count, Listed Count, and Impression Count and break the results down by Account URL and Account Verified. Check completeness, unexpected nulls, duplicate identifiers at [Expected Grain], invalid values, incompatible aggregation levels, and date consistency where the returned fields support those tests. Return rule-level failure counts and a severity-ranked exception sample with account, field, observed value, expected condition, and reproducible validation step. Distinguish source-data issues from extraction or modeling issues, and do not infer uniqueness, join keys, or business meaning unless confirmed in [Data Contract or Source Documentation].

X / Twitter Public Data Freshness and Coverage Monitor

Using the Catchr MCP, monitor X / Twitter Public Data delivery for [Account or All Connected Accounts] across [Lookback Period]. Review Impression Count, Like Count, and Followers Count and break the results down by Account Verified Type and Account Scope. Measure date coverage, latest available record, row or entity volume, missing intervals, and field population against [Expected Schedule] and [Freshness SLA]. Flag stale extraction, flatlined volume, schema coverage changes, and gaps beyond [Absolute and Relative Thresholds]. Return an account-level health summary plus an investigation table with first affected period, supporting fields, likely failure domain stated cautiously, owner, and next validation query. Separate genuine source inactivity from a pipeline issue whenever the evidence cannot distinguish them.

X / Twitter Public Data Drift and Reconciliation Review

Using the Catchr MCP, compare X / Twitter Public Data across [Current Window] and [Baseline Window] for [Account or All Connected Accounts]. Review Followers Count, Reply Count, and Retweet Count and break the results down by Tweet Conversation Id and Tweet Reply To User Id. Profile distributions, null rates, distinct-value counts, volume, and material changes at the declared reporting grain. Reconcile related fields only when their definitions and aggregation levels are compatible. Flag abrupt shifts beyond [Drift Threshold], inconsistent totals, and values that violate [Business or Data Contract Rules]. Produce a reproducible alert table with evidence, severity, probable domain, and next check, followed by a concise assessment of which analyses may be unreliable until each issue is resolved.

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

Connect X / Twitter Public Data to Claude

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How to connect X / Twitter Public Data to Claude ?

Authorize the X / Twitter Public Data account in Catchr

Add the Catchr MCP as a custom connector in Claude

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 X / Twitter Public Data prompts for analyzing real account data.

X / Twitter Public Data to Claude FAQs

What X / Twitter Public Data data can Claude analyze through Catchr?

How granular can X / Twitter Public Data analysis be in Claude?

Can Claude compare X / Twitter Public Data performance across periods or accounts?

Can Claude investigate unusual changes in X / Twitter Public Data?

What types of prompts work best for X / Twitter Public Data analysis in Claude?

Can I combine X / Twitter Public Data with other data sources in Claude?

Can Claude compare multiple X / Twitter Public Data accounts or entities?

What do I need to connect X / Twitter Public Data to Claude with Catchr?

How recent is the X / Twitter Public Data data, and how much history can Claude analyze?

Can Claude change anything in X / Twitter Public Data through Catchr?

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

Can I try the Claude integration before choosing a plan?

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