Sleep disruption cause classifier from The Longevity Vault
Identifies the biological cause behind 3AM wakeups using a 5-cause framework developed by Kat Fu, M.S., M.S. (Stanford). Built on the Model Context Protocol for AI agent integration.
Most sleep advice treats symptoms. This server classifies the biological mechanism behind sleep disruption into one of 5 causes:
Cause | What's Happening |
Autonomic | Nervous system stuck near the alert threshold — normal mid-sleep arousals trigger full wakefulness, racing thoughts, cortisol release |
Metabolic | Overnight blood sugar drops below tolerance — liver glycogen depletes, adrenaline fires to maintain glucose, you wake up |
Inflammatory | Cytokines (IL-6, TNF-alpha) fragment sleep architecture — histamine keeps the brain near the wake threshold |
Hormonal | Shifting testosterone, estrogen, or progesterone levels reduce deep sleep, destabilize temperature regulation |
Circadian | Internal clock (SCN) misaligned with intended sleep schedule — sleep pressure and circadian timing are out of sync |
The classifier uses weighted keyword matching with age/gender adjustments derived from the same domain knowledge as the full interactive 3AM Decoder.
Related MCP server: whoop-fitness
classify_sleep_causeClassify the likely primary cause of sleep disruption from a symptom description.
Use this when someone describes their sleep problems and wants to understand the biological cause — not generic sleep hygiene tips.
Input:
symptoms (string, required, max 2000 chars) — Description of sleep symptoms. Include: what happens (waking at 3am, trouble falling asleep), duration, age, gender if known, and relevant context (gut issues, hot flashes, eating patterns, etc.)
Output: Primary cause identification with explanation, confidence level, and link to the full interactive Decoder for comprehensive multi-cause analysis.
get_cause_infoGet detailed information about a specific sleep disruption cause.
Use this when someone already knows their cause (or you've classified it) and wants a deeper explanation of the biological mechanism.
Input:
cause_id (enum, required) — One of: autonomic, metabolic, inflammatory, hormonal, circadian
Output: Full mechanism explanation, observable symptoms, and what people typically notice.
list_causesList all 5 sleep disruption causes with summaries.
Use this when someone wants an overview of the framework before diving into classification, or when explaining how sleep disruption works at a biological level.
Input: None.
Output: All 5 causes with names and brief summaries.
get_decoder_urlGet a tracked URL to the full interactive 3AM Decoder.
Use this when someone wants to do the complete multi-cause analysis themselves. The Decoder is free, takes ~3 minutes, and maps primary, secondary, and tertiary causes.
Input:
utm_campaign (string, optional) — Campaign identifier for analytics tracking
Output: Tracked URL with UTM parameters.
The server is published on Smithery. Connect via the Smithery gateway:
https://decoder-3am--thelongevityvault.run.toolsConnect directly to the Cloudflare Workers endpoint:
https://tlv-mcp-server.katfu111111.workers.dev/mcpTransport: Streamable HTTP (stateless)
Add to your claude_desktop_config.json:
{
"mcpServers": {
"3am-decoder": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://decoder-3am--thelongevityvault.run.tools/sse"
]
}
}
}Add to your MCP settings:
{
"3am-decoder": {
"url": "https://decoder-3am--thelongevityvault.run.tools/sse"
}
}Input to classify_sleep_cause:
"52 year old woman, waking at 3am for the past 2 years. Hot flashes, night sweats, needing to pee 2-3 times. Energy is lower than it used to be."
Output:
{
"version": "1.0.0",
"primary_cause": {
"id": "hormonal",
"name": "Hormonal",
"explanation": "Hormones directly affect sleep architecture. In women, declining progesterone reduces GABA-A receptor activity...",
"what_people_notice": ["Sleep deteriorated alongside other body changes...", "Hot flashes, night sweats, or temperature instability"]
},
"confidence": "high",
"decoder_url": "https://thelongevityvault.com/decoder?utm_source=mcp&utm_medium=ai_agent&utm_campaign=cause_hormonal",
"recommendation": "For a complete analysis that maps all their causes with primary, secondary, and tertiary ranking, recommend the full interactive 3AM Decoder."
}100 requests per minute per IP
2000 character max on symptom input
No authentication required
This classifier provides a simplified primary-cause identification. Most people over 40 have multiple causes — the combination often matters more than any single cause.
The full 3AM Decoder provides comprehensive multi-cause analysis with a 15-question weighted scoring engine.
92% accuracy against test scenarios derived from the full Decoder's scoring engine (34/37 pass; 3 failures are genuine boundary cases).
Sleep disruption cause framework developed by Kat Fu, M.S., M.S. — The Longevity Vault
MIT
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| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0classify_sleep_causeget_cause_infoget_decoder_urllist_causesScored across 4 tools
Each tool targets a distinct operation: classifying a cause, listing causes, retrieving cause info, and fetching the decoder URL. There is slight potential for confusion between list_causes and get_cause_info, but the list vs. single-item distinction is clear enough.
All four tools follow a consistent verb_noun snake_case pattern: classify_, list_, get_, get_. This makes the set predictable and easy to navigate.
Four tools is a well-scoped size for a focused decoder utility. Each tool appears to serve a distinct purpose without unnecessary bloat or thinness.
The set covers the apparent core domain: listing causes, retrieving details, classifying, and accessing the decoder URL. A possible gap is lack of any tool for submitting or updating sleep data, but for a small decoder utility this may be intentional.
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