Hippo Example Dialog

Demonstrating MCP operations in realistic collaborative sessions

Session: Debugging Authentication Issues

User: Hi again, Claude. I'm having trouble with our authentication flow - users are getting logged out randomly.

Claude: Let me search for any previous insights about authentication issues.

[Invokes: hippo_search_insights(query="authentication logout", context_filter=["debugging authentication"])]

[Returns: 2 insights found]

{
  "insights": [
    {
      "uuid": "prev-auth-001",
      "content": "Session timeouts often appear random but are actually triggered by background API calls failing silently",
      "context": ["debugging authentication flow", "session management issues", "background API failures"],
      "importance": 0.8,
      "current_score": 0.9,
      "days_since_created": 28
    },
    {
      "uuid": "prev-auth-002", 
      "content": "Check network tab for 401/403 errors when users report unexpected logouts",
      "context": ["debugging authentication flow", "network debugging techniques", "user logout issues"],
      "importance": 0.6,
      "current_score": 0.7,
      "days_since_created": 15
    }
  ],
  "total_matching": 2,
  "returned_count": 2,
  "score_distribution": {
    "0.6_to_0.8": 1,
    "0.8_to_1.0": 1
  }
}

I found a couple of previous insights about authentication. One from last month mentioned that session timeouts often appear random when they're actually tied to background API calls failing. Have you checked if there are any failing requests in the network tab?

User: Good point! I see some 401 errors on the /refresh-token endpoint. It looks like the refresh token is expiring before we try to use it.

Claude: Ah, that's a classic race condition. Let me check if we have insights about token refresh timing...

[Invokes: hippo_search_insights(query="token refresh race condition", limit={"offset": 0, "count": 5})]

[Returns: 1 insight found]

{
  "insights": [
    {
      "uuid": "token-race-001",
      "content": "Async token refresh creates race conditions where old token expires before new one is ready - solution is to queue requests during refresh",
      "context": ["debugging authentication flow", "async token handling", "race condition patterns"],
      "importance": 0.9,
      "current_score": 1.2,
      "days_since_created": 45,
      "days_since_score_modified": 12
    }
  ],
  "total_matching": 1,
  "returned_count": 1,
  "score_distribution": {
    "above_1.0": 1
  }
}

Yes, we discovered this pattern before - async token refresh can create race conditions where the old token expires before the new one is ready. The solution was to queue requests during refresh.

User: Right! I remember now. Let me implement that fix...

[30 minutes later]

User: OK, that fixed it! The queuing approach worked perfectly. Make it so?

Claude: Excellent! Let me capture what we learned in this session.

[Invokes: hippo_record_insight(content="Authentication logout issues often caused by race conditions in token refresh - queue requests during refresh to prevent 401 errors", context=["debugging authentication flow", "users getting logged out randomly", "race condition in token refresh"], importance=0.8)]

[Invokes: hippo_record_insight(content="Network tab 401 errors on /refresh-token endpoint indicate token expiry race condition", context=["debugging authentication flow", "users getting logged out randomly", "network debugging techniques"], importance=0.6)]

[Invokes: hippo_record_insight(content="Previous insights about session timeouts helped identify root cause faster than starting from scratch", context=["debugging authentication flow", "using historical insights", "problem-solving efficiency"], importance=0.7)]

Here are the insights I captured from our debugging session, with my suggested reinforcement based on how we used them:

1. abc123: Authentication logout issues often caused by race conditions in token refresh - queue requests during refresh to prevent 401 errors [SUGGEST UPVOTE - this was the key solution we implemented]
2. def456: Network tab 401 errors on /refresh-token endpoint indicate token expiry race condition [SUGGEST UPVOTE - this diagnostic technique led us to the solution]
3. ghi789: Previous insights about session timeouts helped identify root cause faster than starting from scratch [SUGGEST NEUTRAL - true but not particularly novel]

Do these suggestions seem right, or would you reinforce differently?

User: Yeah, that makes sense. Go with your suggestions.

Claude: Perfect!

[Invokes: hippo_reinforce_insight(upvotes=["abc123", "def456"], downvotes=[])]

The first two insights have been reinforced since they directly contributed to solving the problem. The third will age naturally since it wasn't particularly actionable.

Session: Three Weeks Later

User: I'm debugging another auth issue - users can't log in at all now.

Claude: Let me check our authentication insights...

[Invokes: hippo_search_insights(query="authentication login", context_filter=["debugging authentication"], score_range={"min": 0.5})]

[Returns: Previous insights including the reinforced race condition one]

{
  "insights": [
    {
      "uuid": "abc123",
      "content": "Authentication logout issues often caused by race conditions in token refresh - queue requests during refresh to prevent 401 errors",
      "context": ["debugging authentication flow", "users getting logged out randomly", "race condition in token refresh"],
      "importance": 0.8,
      "current_score": 1.6,
      "days_since_created": 21,
      "days_since_score_modified": 21
    },
    {
      "uuid": "def456",
      "content": "Network tab 401 errors on /refresh-token endpoint indicate token expiry race condition",
      "context": ["debugging authentication flow", "users getting logged out randomly", "network debugging techniques"],
      "importance": 0.6,
      "current_score": 1.1,
      "days_since_created": 21,
      "days_since_score_modified": 21
    },
    {
      "uuid": "prev-auth-001",
      "content": "Session timeouts often appear random but are actually triggered by background API calls failing silently",
      "context": ["debugging authentication flow", "session management issues", "background API failures"],
      "importance": 0.8,
      "current_score": 0.7,
      "days_since_created": 49
    }
  ],
  "total_matching": 5,
  "returned_count": 3,
  "score_distribution": {
    "0.6_to_0.8": 1,
    "0.8_to_1.0": 0,
    "above_1.0": 2,
    "below_0.2": 2
  }
}

I found several relevant insights from our previous debugging sessions. The most relevant one is about token refresh race conditions (score 1.6), but since this is about login rather than logout, let me search more broadly...

[Invokes: hippo_search_insights(query="authentication debugging", limit={"offset": 0, "count": 8})]

Based on our previous patterns, I'd suggest checking: network errors, token endpoint responses, and timing issues. What are you seeing in the browser console?

User: Actually, looking at that race condition insight - I think the same queuing logic might apply here. Let me check if login requests are conflicting...

[User investigates and finds the issue]

User: You were right! The login flow had the same race condition. But I want to update that insight to be more general.

Claude: Good idea! Let me modify it to capture the broader pattern.

[Invokes: hippo_modify_insight(uuid="abc123", content="Authentication issues (login/logout) often caused by race conditions in token operations - queue requests during token refresh/creation to prevent timing conflicts", context=["debugging authentication flow", "users getting logged out randomly", "users unable to log in", "race condition in token operations"])]

Updated the insight to cover both login and logout scenarios. Since you took time to generalize it, I've applied an upvote to reflect its increased value.

User: Perfect. Make it so!

[Session continues with normal consolidation...]

Key Patterns Demonstrated

Search Operations

• Context-filtered search: Finding insights from similar situations (context_filter="debugging")
• Keyword search: Looking for specific technical patterns (query="token refresh race condition")
• Score-filtered search: Focusing on high-quality insights (score_range={"min": 0.5})
• Paginated search: Controlling result size (limit={"offset": 0, "count": 8})

Insight Generation

• Consolidation moments: Insights generated during "Make it so" checkpoints
• Importance assessment: AI rates significance (0.6-0.8 range for debugging insights)
• Contextual capture: Specific situational context ("debugging authentication flow")
• Atomic content: Each insight captures one actionable observation

Reinforcement Learning

• Batch feedback: Multiple upvotes/downvotes in single operation
• Score evolution: Upvoted insights gain prominence (1.2 → 1.6 after reinforcement)
• Natural decay: Downvoted insights fade from search results
• Value validation: Insights prove worth through repeated relevance

Insight Evolution

• Content modification: Generalizing insights as understanding deepens
• Implicit reinforcement: Modification signals value (automatic upvote)
• Context preservation: Maintaining situational grounding while broadening applicability

Technical Operation Summary

This workflow demonstrates how Hippo creates a reinforcement learning loop where valuable insights naturally surface more often while less useful ones fade away, creating an increasingly valuable collaborative memory system.