Examples

Integration Examples

Real-world use cases for Pulsar MCP Server in AI agents.

Trading Signals Agent

Monitor crypto communities for trading signals like token launches, airdrops, and partnerships.

Use Case

Your agent needs to:

Monitor dozens of crypto channels 24/7
Detect high-value trading signals
Alert users in real-time
Provide context and source links

MCP Tools Used

search_summaries_by_topic - Find mentions of keywords
semantic_search_messages - Get detailed context
get_channel_summary - Understand channel activity

Agent Instructions

You are a crypto trading signal monitor.

Every 30 minutes, search for:
- "token launch"
- "airdrop"  
- "partnership"
- "listing"

When you find matches:
1. Use semantic search to get message context
2. Check if signal is high-value (20+ mentions or high relevance)
3. Alert user with formatted signal:

🚨 SIGNAL: [Headline]
Channel: [Name]
Keywords: [comma-separated]
Timestamp: [ISO format]
Context: [2-sentence summary]

Example Output

🚨 Trading Signal Detected

Channel: Virtuals Protocol
Headline: MetaInsideAI Token Launch Announcement
Keywords: token, launch, airdrop, AI agent
Timestamp: 2025-01-15T14:00:00Z

Context: New AI agent token launching next week with airdrop to 
VIRTUAL stakers. Community showing strong bullish sentiment with 
127 new members joining in last 12 hours.

[View Channel] [Set Alert]

Research Assistant Agent

Answer user questions about crypto projects using community intelligence.

Use Case

Your agent needs to:

Answer "What's happening with [project]?"
Provide recent developments and sentiment
Cite community sources
Track multiple channels

MCP Tools Used

get_channel_summary - Get latest 12-hour summaries
semantic_search_messages - Find relevant discussions
list_all_channels - Discover available channels

Agent Instructions

You are a crypto research assistant.

When user asks about a project:
1. Get latest 3 summaries using get_channel_summary
2. Search for specific details using semantic_search_messages
3. Synthesize comprehensive answer

Format responses as:
📊 Key Developments (Last 36h):
[Numbered list with headlines and insights]

📈 Community Growth: [member delta]
💬 Sentiment: [bullish/bearish/mixed with reason]
🔑 Keywords: [top 5 keywords]

Source: [Channel name]
Last updated: [ISO timestamp]

Example Output

Based on the latest community activity (last 36 hours):

📊 Key Developments:

1. **MetaInsideAI 20x Growth**
   AI agent generated 20x returns in first week.
   Strong bullish sentiment in community.

2. **veVIRTUALS Staking Rewards**  
   Increased rewards for governance participants.
   APY details coming in next announcement.

3. **Gaming Partnership Talks**
   Unconfirmed reports of talks with major platform.
   Community speculating about integration timeline.

📈 Community Growth: +127 members (4,287 → 4,414)

💬 Sentiment: Highly bullish - excitement around agent 
   launches and staking opportunities

🔑 Keywords: AI, agent, staking, gaming, partnership

Source: Virtuals Protocol official channel
Last updated: 2025-01-15T18:00:00Z

Daily Briefing Bot

Generate daily intelligence briefings for subscribers.

Use Case

Your agent needs to:

Compile daily crypto news
Highlight active channels
Track trending topics
Deliver at scheduled time

MCP Tools Used

list_available_summaries - Get overview of all channels
get_channel_summary - Get details for active channels
search_summaries_by_topic - Track trending topics

Agent Instructions

You are a daily crypto briefing generator.

Every day at 7:00 AM UTC:
1. Use list_available_summaries to find channels with activity
2. Get summaries for top 10 most active channels
3. Group by category (AI, L1, DeFi)
4. Format as daily briefing

Template:
☀️ Crypto Community Briefing - [Date]

📰 Top Stories Today:
[For each channel: emoji icon, headline, sentiment, member growth]

🔥 Trending Topics:
[Top 5 keywords across all channels]

📊 Overall Sentiment: [analysis]

[Customize Channels] [View Full Details]

Example Output

☀️ Crypto Community Briefing - January 15, 2025

📰 Top Stories Today:

1. FETCH.AI 🤖
   → AWS Partnership Announced
   Community: 🔥 Very Bullish (+57 members)

2. VIRTUALS 🎮  
   → MetaInsideAI 20x Returns
   Community: 🚀 Extremely Active (+127 members)

3. SOLANA ⚡
   → Network Upgrade Scheduled
   Community: ⚠️ Mixed Sentiment (+23 members)

4. TONCOIN 💎
   → 15% Staking APY Launch
   Community: ✅ Positive Response (+45 members)

🔥 Trending Topics:
AI agents, staking, partnership, upgrade, rewards

📊 Overall Sentiment: Bullish across AI/agent sector, 
   mixed on infrastructure updates

[Customize Channels] [View Full Details]

Multi-Channel Topic Tracker

Track specific topics across all monitored communities.

Use Case

Your agent needs to:

Monitor "staking rewards" across all channels
Compare activity levels
Identify trends
Generate periodic reports

MCP Tools Used

search_by_timeframe - Search across time range
search_summaries_by_topic - Find keyword matches
semantic_search_messages - Get relevant context

Agent Instructions

You are a topic tracking agent.

When user requests topic tracking:
1. Use search_by_timeframe with user's topic and date range
2. Group results by channel
3. Count mentions and analyze sentiment
4. Generate comparative report

Format:
📊 Topic Report: "[topic]"
Period: [timeframe]

🔥 Most Active Channels:
[Ranked list with mention counts and key themes]

📈 Sentiment Trend: [direction and reasoning]

🔑 Key Themes:
[Bullet list of patterns]

💡 Insight: [1-sentence takeaway]

Example Output

📊 Topic Report: "staking rewards"
Period: Last 7 days

🔥 Most Active Channels:
1. Toncoin (23 mentions) - New 15% APY program
2. Solana (18 mentions) - Liquid staking updates  
3. Fetch.ai (12 mentions) - AI agent staking pool

📈 Sentiment Trend: ↗️ Increasingly positive

🔑 Key Themes:
• High APY competition (12-15% range)
• Liquid staking gaining popularity
• Governance participation incentives

💡 Insight: Multi-chain staking competition heating up.
         Users comparing yields across ecosystems.

Sentiment Tracker

Monitor sentiment changes for specific tokens or projects over time.

Use Case

Your agent needs to:

Track sentiment for Virtuals Protocol
Monitor member growth trends
Detect narrative shifts
Alert on major changes

MCP Tools Used

get_channel_summary - Get historical summaries
semantic_search_messages - Find sentiment indicators

Agent Instructions

You are a sentiment tracking agent.

For each channel you monitor:
1. Get last 10 summaries (5 days of 12-hour periods)
2. Extract member growth deltas
3. Track keyword frequency changes
4. Detect sentiment shifts

Alert when:
- Member growth >50 in single period
- Negative keywords spike (exploit, hack, outage)
- Sentiment reverses (bullish → bearish or vice versa)

Format alerts:
🚨 SENTIMENT ALERT: [Channel]
Change: [from] → [to]
Trigger: [reason]
Member Impact: [delta]
Action: [recommendation]

Best Practices

Combine Tools for Context

Don't rely on single tool - build complete picture:

Overview - list_available_summaries
Details - get_channel_summary
Deep dive - semantic_search_messages
Cross-reference - search_summaries_by_topic

Refresh Strategically

Summaries: Generated every 12 hours (06:00 & 18:00 UTC)
Check ~10 minutes after generation for fresh data
Don't poll more than every 30 minutes

Handle Rate Limits

With bearer token you get 60 req/min:

Batch queries when possible
Stagger automated checks
Cache results appropriately

Parse MCP Responses

MCP tools return formatted strings. Your agent should:

Extract key information (channel names, timestamps, keywords)
Structure for your application's needs
Store for trend analysis if building historical views

Framework Integration

LangChain

Add Pulsar as tool to your LangChain agent:

from langchain.agents import initialize_agent
from langchain.agents import AgentType

# Your MCP client will handle tool discovery
# Just configure Pulsar in MCP settings

agent = initialize_agent(
    tools=[],  # MCP tools auto-discovered
    llm=your_llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
)

CrewAI

Add Pulsar to your crew's capabilities:

from crewai import Agent, Crew

crypto_analyst = Agent(
    role="Crypto Community Analyst",
    goal="Monitor communities for signals",
    # MCP tools available automatically
    backstory="Expert at analyzing community sentiment"
)

crew = Crew(agents=[crypto_analyst])

Eliza (ElizaOS)

Configure in your Eliza character file:

{
  "name": "CryptoAnalyst",
  "clients": ["discord", "twitter"],
  "mcp": {
    "servers": {
      "pulsar": {
        "type": "sse",
        "url": "https://mcp.askpulsar.com/mcp/sse"
      }
    }
  }
}

Testing Your Integration

1. Verify Connection

Ask your agent: "List all channels you can monitor"

Expected: Should list 25+ channels with names

2. Test Summary Retrieval

Ask: "Summarize Virtuals Protocol"

Expected: Should return formatted summary with developments

3. Test Search

Ask: "What are people saying about AI agents?"

Expected: Should return relevant messages with context

4. Verify Tools

Ask: "What Pulsar tools do you have access to?"

Expected: Should list all 7 tools with descriptions

Need Help?

📧 Email: [email protected] 💬 Telegram: @olenovyk 🐦 Twitter: @olenovyk

Share your implementation and we'll feature it here!

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Last updated 29 days ago

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Integration Examples

Trading Signals Agent

Use Case

MCP Tools Used

Agent Instructions

Example Output

Research Assistant Agent

Use Case

MCP Tools Used

Agent Instructions

Example Output

Daily Briefing Bot

Use Case

MCP Tools Used

Agent Instructions

Example Output

Multi-Channel Topic Tracker

Use Case

MCP Tools Used

Agent Instructions

Example Output

Sentiment Tracker

Use Case

MCP Tools Used

Agent Instructions

Best Practices

Combine Tools for Context

Refresh Strategically

Handle Rate Limits

Parse MCP Responses

Framework Integration

LangChain

CrewAI

Eliza (ElizaOS)

Testing Your Integration

1. Verify Connection

2. Test Summary Retrieval

3. Test Search

4. Verify Tools

Need Help?