anysite Brand Reputation Monitoring

Monitor and protect your brand reputation across social media platforms. Track mentions, analyze sentiment, and identify issues before they escalate.

Overview

• Track brand mentions across social platforms
• Analyze sentiment (positive, negative, neutral)
• Monitor conversations about your brand
• Identify reputation risks and crisis signals
• Measure brand health over time

Coverage: 65% - Pivoted from review platforms to social media monitoring; strong for Twitter, Reddit, Instagram, YouTube, LinkedIn

v2 Tool Interface

All data fetching uses the anysite MCP v2 universal meta-tools:

• execute(source, category, endpoint, params) — fetch data from any source. Returns first page + cache_key.
• get_page(cache_key, offset, limit) — paginate through results when next_offset is returned.
• query_cache(cache_key, conditions, sort_by, aggregate, group_by) — filter, sort, or aggregate cached data without new API calls.
• export_data(cache_key, format) — export full dataset as CSV, JSON, or JSONL for reports.

Always call discover(source, category) first if unsure about endpoint names or params.

Error Handling

v2 responses may include llm_hint fields with guidance on how to fix errors (e.g., wrong URN format, missing params). Always check llm_hint in error responses before retrying.

Supported Platforms

• Twitter/X: Real-time mentions, sentiment, viral content
• Reddit: Community discussions, detailed feedback, sentiment
• Instagram: Visual brand mentions, hashtag tracking, influencer posts
• YouTube: Video mentions, comment sentiment, brand coverage
• LinkedIn: Professional mentions, company updates, B2B sentiment

Quick Start

Step 1: Set Up Monitoring

Define:

• Brand keywords (company name, product names, misspellings)
• Platforms to monitor (Twitter, Reddit, Instagram, etc.)
• Monitoring frequency (real-time, daily, weekly)
• Alert thresholds (negative sentiment, volume spikes)

Step 2: Search for Mentions

Platform searches:

Twitter:    execute("twitter", "search", "search_posts", {"query": "brand name", "count": 100})
Reddit:     execute("reddit", "search", "search_posts", {"query": "brand name", "count": 100})
Instagram:  execute("instagram", "search", "search_posts", {"query": "#brandname", "count": 100})
LinkedIn:   execute("linkedin", "post", "search_posts", {"keywords": "brand name", "count": 50})

Each call returns a cache_key — use it for pagination, filtering, and export.

Step 3: Analyze Sentiment

For each mention:

• Classify: Positive, negative, neutral
• Categorize: Complaint, praise, question, general
• Prioritize: Urgency, reach, influence

Use query_cache(cache_key, conditions=[...], sort_by=...) to filter high-engagement or negative mentions without re-fetching.

Step 4: Take Action

Based on findings:

• Respond to negative mentions
• Amplify positive feedback
• Address product issues
• Engage with community

Common Workflows

Workflow 1: Daily Brand Monitoring

Scenario: Monitor brand mentions across all platforms daily

Steps:

1. Search All Platforms

# Twitter (real-time pulse)
execute("twitter", "search", "search_posts", {"query": "brand name OR @brandhandle", "count": 100})
→ Returns cache_key_twitter; filter last 24h with from_date param (timestamp)

# Reddit (detailed discussions)
execute("reddit", "search", "search_posts", {"query": "brand name", "count": 50, "time_filter": "day"})
→ Returns cache_key_reddit

# Instagram (visual mentions)
execute("instagram", "search", "search_posts", {"query": "#brandname OR brand name", "count": 50})
→ Returns cache_key_instagram

# LinkedIn (professional mentions)
execute("linkedin", "post", "search_posts", {"keywords": "brand name", "count": 20})
→ Returns cache_key_linkedin

# YouTube (video coverage)
execute("youtube", "search", "search_videos", {"query": "brand name review OR brand name unboxing", "count": 20})
→ Returns cache_key_youtube

If any result includes next_offset, fetch more with:

get_page(cache_key, offset=next_offset, limit=50)

2. Classify Mentions

Use query_cache to sort and filter cached results:

# Find high-engagement mentions across platforms
query_cache(cache_key_twitter, sort_by=[{"field": "favorite_count", "order": "desc"}])
query_cache(cache_key_reddit, sort_by=[{"field": "vote_count", "order": "desc"}])

For each mention:

Sentiment:
- Positive: Praise, recommendation, satisfaction
- Negative: Complaint, criticism, problem
- Neutral: Question, general mention, factual

Category:
- Product feedback
- Customer service issue
- Feature request
- General discussion
- Competitor comparison

3. Prioritize Issues

High Priority:
- Negative + High reach (viral potential)
- Multiple complaints about same issue
- Influencer negative mention
- Legal/safety concerns

Medium Priority:
- Individual complaints
- Feature requests
- Questions
- General feedback

Low Priority:
- Positive mentions
- Neutral discussions
- General brand awareness

4. Generate Daily Report

Export data for reporting:

export_data(cache_key_twitter, "csv")
export_data(cache_key_reddit, "csv")
→ Returns download URLs for each dataset

Summary:
- Total mentions (by platform)
- Sentiment breakdown (% positive/negative/neutral)
- Top issues identified
- Viral/trending mentions
- Recommended actions

Expected Output:

• Daily mention count: 50-200
• Sentiment distribution
• Top 5 issues to address
• Action items for team

Workflow 2: Crisis Detection and Management

Scenario: Identify and track potential PR crises

Steps:

1. Monitor for Anomalies

Track baseline:
- Average mentions per day
- Average sentiment score
- Typical engagement levels

Alert triggers:
- Mentions >2x baseline
- Negative sentiment >50%
- Viral negative content (high engagement)

2. Deep Dive on Spikes

When alert triggered:

execute("twitter", "search", "search_posts", {"query": "brand name", "count": 500})
→ Identify what's driving spike; use get_page() to load all results

execute("reddit", "search", "search_posts", {"query": "brand name", "count": 200})
→ Check community discussions

For viral posts:
  # Get specific Reddit post details and comments
  execute("reddit", "posts", "posts", {"post_url": "<reddit_post_url>"})
  execute("reddit", "posts", "posts_comments", {"post_url": "<reddit_post_url>"})
  → Analyze reach and engagement, read comments for context

  # For viral tweets, scrape the tweet URL directly
  execute("webparser", "parse", "parse", {"url": "<tweet_url>"})
  → Get tweet details and engagement metrics

3. Assess Crisis Severity

Severity factors:
- Volume (how many mentions)
- Velocity (how fast growing)
- Reach (influencer involvement, media coverage)
- Sentiment (how negative)
- Validity (legitimate issue vs. misunderstanding)

Use query_cache to analyze cached data:
query_cache(cache_key, aggregate=[{"function": "count"}])
→ Total mention count without re-fetching

4. Track Crisis Evolution

Hourly monitoring:
- Mention volume trend
- Sentiment shifts
- Platform spread
- Media pickup
- Official response impact

5. Measure Resolution

Track until:
- Volume returns to baseline
- Sentiment improves
- No new negative mentions for 24-48h

Expected Output:

• Crisis timeline
• Mention volume graph
• Sentiment tracking
• Key influencers/posts
• Response effectiveness

Workflow 3: Competitive Reputation Benchmarking

Scenario: Compare brand sentiment vs. competitors

Steps:

1. Define Competitors

List 3-5 main competitors

2. Gather Mentions for All

For brand + each competitor:
  execute("twitter", "search", "search_posts", {"query": "<brand>", "count": 200})
  execute("reddit", "search", "search_posts", {"query": "<brand>", "count": 100})
  execute("linkedin", "post", "search_posts", {"keywords": "<brand>", "count": 50})

Each returns a cache_key — use get_page() if next_offset indicates more data.

3. Calculate Brand Health Scores

For each brand:

Use query_cache to aggregate metrics from cached results:
  query_cache(cache_key, aggregate=[{"function": "count"}])
  → Total mention volume

  query_cache(cache_key, aggregate=[{"function": "avg", "field": "favorite_count"}])
  → Average engagement per mention

Mention Volume: Total mentions
Sentiment Score: (Positive - Negative) / Total
Engagement Rate: Avg engagement per mention
Share of Voice: Your mentions / Total category mentions

4. Analyze Strengths/Weaknesses

Compare:
- What are competitors praised for?
- What are competitors criticized for?
- Where do we excel?
- Where do we fall short?

5. Identify Opportunities

Look for:
- Unmet customer needs (complaints about competitors)
- Messaging gaps (what they're not saying)
- Product differentiation opportunities
- Customer service advantages

Expected Output:

• Competitive sentiment matrix
• Brand health scores comparison
• Strength/weakness analysis
• Strategic opportunities

MCP Tools Reference (v2)

Twitter/X

• execute("twitter", "search", "search_posts", {"query": ..., "count": N}) — Find brand mentions. Supports from_date, to_date, min_likes, min_retweets, language filters.
• execute("twitter", "user", "user", {"user": ...}) — Check brand profile stats
• execute("twitter", "user", "user_posts", {"user": ..., "count": N}) — Monitor brand account posts

Reddit

• execute("reddit", "search", "search_posts", {"query": ..., "count": N}) — Find discussions. Supports sort (relevance/hot/top/new) and time_filter (day/week/month/year).
• execute("reddit", "posts", "posts", {"post_url": ...}) — Get post details and sentiment
• execute("reddit", "posts", "posts_comments", {"post_url": ...}) — Deep dive on discussions

Instagram

• execute("instagram", "search", "search_posts", {"query": ..., "count": N}) — Find visual mentions
• execute("instagram", "post", "post", {"post": ...}) — Analyze mention engagement
• execute("instagram", "post", "post_comments", {"post": ..., "count": N}) — Read feedback

YouTube

• execute("youtube", "search", "search_videos", {"query": ..., "count": N}) — Find video mentions
• execute("youtube", "video", "video", {"video": ...}) — Get video details
• execute("youtube", "video", "video_comments", {"video": ..., "count": N}) — Analyze sentiment

LinkedIn

• execute("linkedin", "post", "search_posts", {"keywords": ..., "count": N}) — Professional mentions
• execute("linkedin", "company", "company_posts", {"urn": {"type": "company", "value": "<id>"}, "count": N}) — Monitor own company posts. Requires company URN from execute("linkedin", "company", "company", {"company": "<alias>"}).

Pagination, Caching & Export

• get_page(cache_key, offset, limit) — Load next page of results from any execute() call
• query_cache(cache_key, conditions, sort_by, aggregate, group_by) — Filter/sort/aggregate cached data without new API calls
• export_data(cache_key, "csv"|"json"|"jsonl") — Export full dataset as downloadable file

Sentiment Analysis Framework

Manual Sentiment Classification:

Positive Indicators:

• "Love", "great", "amazing", "best"
• Recommendations ("highly recommend")
• Repeat purchase ("buying again")
• Comparisons ("better than X")

Negative Indicators:

• "Disappointed", "worst", "terrible", "awful"
• Problems ("doesn't work", "broken")
• Comparisons ("X is better")
• Demands ("need refund", "fix this")

Neutral Indicators:

• Questions without sentiment
• Factual statements
• General mentions
• Informational content

Sentiment Score:

Score = (Positive mentions - Negative mentions) / Total mentions x 100

+50 to +100: Excellent
+20 to +49: Good
-19 to +19: Neutral/Mixed
-20 to -49: Poor
-50 to -100: Critical

Monitoring Metrics

Volume Metrics:

• Total mentions per day/week/month
• Mentions by platform
• Trend over time

Sentiment Metrics:

• Sentiment distribution (% positive/negative/neutral)
• Sentiment score (net promoter style)
• Sentiment trend over time

Engagement Metrics:

• Average likes/shares per mention
• Viral mentions (>1000 engagements)
• Influencer mentions

Issue Tracking:

• Top complaints (by frequency)
• Product/service issues mentioned
• Feature requests
• Customer service mentions

Output Formats

Chat Summary:

• Daily mention highlights
• Sentiment overview
• Top issues identified
• Recommended actions

CSV Export (via export_data(cache_key, "csv")):

• Mention list with sentiment
• Platform, date, reach
• Issue categorization

JSON Export (via export_data(cache_key, "json")):

• Complete mention data
• Time-series sentiment
• Engagement metrics

Reference Documentation

• MONITORING_GUIDE.md - Best practices for brand monitoring, crisis response protocols, and sentiment analysis techniques

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Ready to monitor your brand? Ask Claude to help you track mentions, analyze sentiment, or identify reputation risks across social platforms!