Real-World Test Scenarios
Practical test scenarios and real-world use cases for the ActivityPub MCP Server. Learn through hands-on examples, comprehensive case studies, and step-by-step walkthroughs.
🎯 Scenario Categories
🔬 Research & Analysis
Academic research, market analysis, and trend identification
📊 Community Management
Understanding communities, monitoring engagement, and growth analysis
🚀 Business Intelligence
Brand monitoring, competitor analysis, and market insights
📰 Journalism & Media
Source verification, story research, and trend tracking
🔬 Research & Analysis Scenarios
Scenario 1: Academic Research on Open Source Communities
Objective:
Research the structure and dynamics of open source software communities in the fediverse for an academic paper.
Step-by-Step Process:
1. Initial Discovery
Query: "Find all fediverse instances focused on open source software development"
Expected Tools: discover-instances with "open source" query
Expected Results: List of instances like fosstodon.org, floss.social, etc.
2. Instance Analysis
Query: "Analyze the top 5 open source instances - their size, rules, and community focus"
Expected Tools: get-instance-info for each instance
Expected Results: Comparative data on user counts, posting activity, and community guidelines
3. Key Actor Identification
Query: "Identify the most influential open source developers and maintainers on these instances"
Expected Tools: discover-actor for prominent users
Expected Results: Profiles of key contributors with follower counts and project affiliations
4. Content Analysis
Query: "Analyze recent discussions about programming languages, tools, and project management"
Expected Tools: fetch-timeline for key actors
Expected Results: Trending topics, discussion themes, and community concerns
5. Network Mapping
Query: "Map the connections between different open source communities and identify collaboration patterns"
Expected Tools: Cross-instance actor discovery and relationship analysis
Expected Results: Network diagram showing inter-community connections
Expected Outcomes:
- Comprehensive dataset of open source fediverse communities
- Analysis of community structures and governance models
- Identification of key influencers and thought leaders
- Understanding of collaboration patterns and knowledge sharing
- Data suitable for academic publication
Scenario 2: Climate Change Discourse Analysis
Objective:
Analyze how climate change is discussed across different fediverse communities to understand public sentiment and information flow.
Research Questions:
- Which instances have the most active climate discussions?
- How do different communities frame climate issues?
- Who are the key voices in climate discourse?
- What solutions and actions are being promoted?
Methodology:
Data Collection
Use discover-instances to find climate-focused instances, then search-content for climate-related posts across multiple instances.
Actor Analysis
Identify climate scientists, activists, and organizations using discover-actor and analyze their posting patterns.
Content Categorization
Classify posts by topic (policy, science, activism, solutions) and sentiment (optimistic, pessimistic, neutral).
Network Analysis
Map information flow between instances and identify key information brokers.
📊 Community Management Scenarios
Scenario 3: New Instance Community Building
Objective:
You're launching a new Mastodon instance focused on sustainable technology and want to understand the landscape and build connections.
Strategic Approach:
1. Competitive Analysis
Query: "Find existing instances focused on sustainability, green technology, and environmental topics"
Analysis: Study their community guidelines, user engagement, and content themes
2. Influencer Identification
Query: "Identify key voices in sustainable technology who might be interested in a new community"
Outreach: Understand their current instance affiliations and engagement patterns
3. Content Strategy Development
Query: "Analyze what types of sustainable technology content get the most engagement"
Planning: Develop content themes and posting strategies based on successful patterns
4. Federation Strategy
Query: "Identify instances that would be good federation partners for a sustainable tech community"
Networking: Plan outreach to compatible communities for cross-pollination
Scenario 4: Community Health Monitoring
Objective:
Monitor the health and engagement of your existing fediverse community to identify trends and potential issues.
Monitoring Framework:
Engagement Metrics
- Post frequency and timing patterns
- Reply and boost ratios
- New user onboarding success
- Active user retention rates
Content Quality
- Discussion depth and quality
- Topic diversity and focus
- Knowledge sharing patterns
- Community-generated content
Community Dynamics
- Inter-user interaction patterns
- Conflict resolution effectiveness
- Moderation workload and issues
- Cross-instance relationship health
🚀 Business Intelligence Scenarios
Scenario 5: Brand Monitoring and Reputation Management
Objective:
Monitor mentions of your brand across the fediverse and understand public sentiment and engagement.
Monitoring Strategy:
Direct Mentions
Track explicit mentions of brand name, products, and key personnel across instances
Tools: search-content with brand keywords
Industry Discussions
Monitor broader industry conversations that might impact brand perception
Tools: discover-instances for industry-specific communities
Competitor Analysis
Track competitor mentions and compare engagement levels
Tools: fetch-timeline for competitor-focused accounts
Influencer Relations
Identify and monitor key industry influencers and their brand interactions
Tools: discover-actor for industry thought leaders
Scenario 6: Market Research for Product Launch
Objective:
Research market sentiment and identify potential early adopters for a new privacy-focused communication tool.
Research Methodology:
1. Target Audience Identification
Query: "Find communities discussing privacy, security, and digital rights"
Goal: Identify instances and actors most concerned with privacy issues
2. Pain Point Analysis
Query: "Analyze discussions about current communication tools and their limitations"
Goal: Understand user frustrations and unmet needs
3. Feature Validation
Query: "Research what features privacy-conscious users value most in communication tools"
Goal: Validate product features against real user preferences
4. Early Adopter Profiling
Query: "Identify users who frequently try new privacy tools and share their experiences"
Goal: Build a list of potential beta testers and early advocates
📰 Journalism & Media Scenarios
Scenario 7: Breaking News Source Verification
Objective:
Verify sources and gather additional context for a breaking news story spreading across the fediverse.
Verification Process:
1. Source Identification
Use discover-actor to verify the credibility and background of original sources
2. Cross-Reference Checking
Search for corroborating accounts and additional sources across multiple instances
3. Timeline Reconstruction
Use fetch-timeline to understand the chronology of events and information flow
4. Expert Opinion Gathering
Identify and contact relevant experts who have commented on the story
Scenario 8: Investigative Journalism Research
Objective:
Research a complex story about corporate influence in environmental policy using fediverse sources.
Investigation Framework:
Source Network Mapping
Map connections between environmental activists, policy experts, and corporate representatives
Information Trail Following
Track how information and narratives spread through different communities
Expert Testimony Collection
Identify and interview subject matter experts active in relevant fediverse communities
Public Sentiment Analysis
Gauge public reaction and understanding of the issues across different demographics
🧪 Testing Protocols
Scenario Validation Checklist
- ✅ All required tools are accessible and functional
- ✅ Data quality meets research standards
- ✅ Privacy and ethical guidelines are followed
- ✅ Results are reproducible and verifiable
- ✅ Methodology is documented and transparent
Performance Benchmarks
- ⏱️ Query response time under 30 seconds
- 📊 Data accuracy above 95%
- 🔄 Cache hit ratio above 70%
- 🚫 Error rate below 5%
- 📈 Successful completion rate above 90%
Quality Assurance
- 🔍 Cross-validate findings with multiple sources
- 📝 Document all queries and methodologies
- 🔄 Test scenarios with different parameters
- 👥 Peer review of analysis and conclusions
- 📊 Statistical significance testing where applicable