MCP Tools Overview

Wake Intelligence provides a comprehensive suite of Model Context Protocol (MCP) tools for managing temporal intelligence. All tools are accessible through Claude Desktop or any MCP-compatible client.

Tool Categories

Core Context Management

Essential tools for saving, loading, and searching contexts.

Tool	Purpose	Layer
save_context	Save conversation context with AI enhancement	All 3
load_context	Retrieve contexts for a project	Layer 2
search_context	Search contexts by keywords	Layer 2

Layer 1: Causality (Past - WHY)

Tools for understanding decision history and causal relationships.

Tool	Purpose
reconstruct_reasoning	Explain WHY a context was created
build_causal_chain	Trace decision history backwards
get_causality_stats	Analytics on causal relationships

Layer 2: Memory (Present - HOW)

Tools for managing memory tiers and access patterns.

Tool	Purpose
get_memory_stats	View memory tier distribution
recalculate_memory_tiers	Update tier classifications
prune_expired_contexts	Clean up old contexts

Layer 3: Propagation (Future - WHAT)

Tools for prediction and pre-fetching optimization.

Tool	Purpose
update_predictions	Refresh prediction scores
get_high_value_contexts	Retrieve likely-needed contexts
get_propagation_stats	Analytics on predictions

Quick Examples

Save a Context

Claude, save this context:
"Completed database migration 0004 for Layer 3. All prediction columns added successfully."

Project: wake-intelligence
Action type: implementation

Load Recent Contexts

Claude, load contexts for project "wake-intelligence"

Reconstruct Reasoning

Claude, why did we create context [context-id]?

Check Memory Stats

Claude, show me memory statistics for "wake-intelligence"

Update Predictions

Claude, update predictions for project "wake-intelligence"

Get High-Value Contexts

Claude, what contexts am I most likely to need next for "wake-intelligence"?

Tool Design Principles

All Wake Intelligence tools follow these principles:

1. Observable Inputs

Every parameter is based on observable, measurable data:

• Project names (strings)
• Context IDs (UUIDs)
• Time windows (hours, days)
• Score thresholds (0.0-1.0)

2. Semantic Outputs

Results include human-readable explanations:

• Memory tier names (ACTIVE, RECENT, ARCHIVED, EXPIRED)
• Action types (decision, implementation, refactor)
• Prediction reasons (recently_accessed, causal_chain_root)

3. Composable Operations

Tools can be chained together:

1. get_high_value_contexts → [list of IDs]
2. load_context → [context details]
3. reconstruct_reasoning → [decision history]

4. Bounded Results

All queries have sensible limits to prevent overwhelming responses:

• Default limit: 10 results
• Maximum limit: 100 results
• Pagination support (coming soon)

Integration Examples

Claude Desktop Workflow

## Daily Standup

Claude, do these in sequence:

1. Load contexts for project "daily-standup" from the last 24 hours
2. Show me memory stats to see what's ACTIVE
3. Update predictions so we can prefetch tomorrow's likely contexts
4. Get high-value contexts (score > 0.7) to prepare for tomorrow

Automated Context Management

## Weekly Cleanup

Claude:

1. Show memory stats for all projects
2. Prune expired contexts (older than 30 days)
3. Recalculate memory tiers for all contexts
4. Update predictions for active projects

Causal Analysis

## Decision Audit Trail

Claude:

1. Search for contexts with tag "architecture-decision"
2. For each result, build the causal chain
3. Reconstruct reasoning to understand WHY decisions were made
4. Show causality stats to identify patterns

Advanced Usage

Prediction-Based Workflow

Use Layer 3 predictions to optimize your AI workflows:

# Morning Routine

Claude:

1. Get high-value contexts (score > 0.8) for today's projects
2. Load those contexts proactively
3. Check propagation stats to see prediction accuracy
4. If accuracy is low, update predictions with fresh data

Memory Tier Management

Leverage automatic tier classification:

# Optimize Storage

Claude:

1. Get memory stats for all projects
2. Identify projects with > 50 EXPIRED contexts
3. Prune expired contexts for those projects
4. Recalculate tiers to refresh classifications

Causal Chain Navigation

Trace decision history:

# Architecture Review

Claude:

1. Search for "database-migration" contexts
2. Build causal chain from latest migration
3. Reconstruct reasoning for each step
4. Identify which decisions led to current architecture

Tool Response Format

All tools return structured JSON responses following MCP protocol:

{
  "content": [
    {
      "type": "text",
      "text": "Human-readable summary"
    },
    {
      "type": "text",
      "text": "Detailed results in markdown"
    }
  ]
}

Error Handling

Wake Intelligence provides clear error messages:

{
  "error": {
    "code": "CONTEXT_NOT_FOUND",
    "message": "Context with ID 'abc-123' does not exist",
    "details": {
      "contextId": "abc-123",
      "searchedIn": "wake-intelligence"
    }
  }
}

Performance Considerations

Tool Execution Time

Tool Category	Typical Response Time
Simple queries (load, search)	< 100ms
Layer 1 analysis (causal chains)	100-300ms
Layer 2 stats (memory analytics)	200-500ms
Layer 3 predictions (batch updates)	500ms-2s

Rate Limits

• Per minute: 60 requests
• Per hour: 1000 requests
• Concurrent: 5 simultaneous requests

Next Steps

• Explore individual tool documentation for detailed parameters
• Learn about the 3-Layer Architecture
• Check out API Reference for programmatic access

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