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  1. Home
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  3. Nefesh β€” Real Time Human State Awareness for AI
Nefesh β€” Real Time Human State Awareness for AI logo
Health: ActiveRecent health check succeeded.Last checked 9/21/2026, 3:46:27 PM

Nefesh β€” Real Time Human State Awareness for AI

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View RepositoryVisit Website

Fuses biometric signals into a stress score (0-100) for AI adaptation. MCP + A2A native.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Remote HTTP
Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "nefesh-real-time-human-state-awareness-for-ai": {
      "url": "https://mcp.nefesh.ai/mcp"
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Tool Schemas (6) Directory Badge Claim listing AlternativesπŸ’¬ More in Communication

Capabilities & Tool Schemas (6) ~231 tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Self-reported Self-reportedParsed from the repository README, not verified against a live server β€” may be incomplete or out of date.

Inspect callable tools, capabilities, and parameters exposed to AI agents by Nefesh β€” Real Time Human State Awareness for AI.

request_api_key

Request a free API key. You MUST ask the user for their real email first. Do not invent or guess emails. The user receives a verification link they must click. Poll with `check_api_key_status` until ready.

check_api_key_status

Poll for API key activation using the same email the user provided. Returns `pending` or `ready` with API key.

get_human_state

Get stress state (0-100), `suggested_action` (maintain/simplify/de-escalate/pause), and `adaptation_effectiveness` β€” a closed-loop showing whether your previous action reduced stress.

ingest

Send biometric signals (heart rate, HRV, voice tone, expression, sentiment, 30+ fields) and get unified state back. Include `subject_id` for trigger memory.

get_trigger_memory

Get psychological trigger profile β€” which topics cause stress (active) and which have been resolved over time.

get_session_history

Get timestamped state history with trend (rising/falling/stable).

Documentation Overview

Nefesh MCP + A2A Server

A Model Context Protocol and Agent-to-Agent (A2A) server that gives AI agents real-time awareness of human physiological state.

What it does

Send sensor data (heart rate, voice, facial expression, text sentiment), get back a unified state with a machine-readable action your agent can follow directly. Zero prompt engineering required.

On the 2nd+ call, the response includes adaptation_effectiveness β€” telling your agent whether its previous approach actually worked. A closed-loop feedback system for self-improving agents.

Adaptation Effectiveness (Closed-Loop)

Most APIs give you a state. Nefesh tells you whether your reaction to that state actually worked.

On the 2nd+ call within a session, every response includes:

config.json
{
  "state": "focused",
  "stress_score": 45,
  "suggested_action": "simplify_and_focus",
  "adaptation_effectiveness": {
    "previous_action": "de-escalate_and_shorten",
    "previous_score": 68,
    "current_score": 45,
    "stress_delta": -23,
    "effective": true
  }
}

Your agent can read effective: true and know its previous de-escalation worked. If effective: false, the agent adjusts its strategy. No other human-state system provides this feedback loop.

Setup

Option A: Connect first, get a key through your agent (fastest)

Add the config without an API key β€” your agent will get one automatically.

config.json
{
  "mcpServers": {
    "nefesh": {
      "url": "https://mcp.nefesh.ai/mcp"
    }
  }
}

Then ask your agent:

"Get me a free Nefesh API key using my email address"

The agent calls request_api_key β†’ you click one email link β†’ the agent picks up the key. No signup form, no manual copy-paste. After that, add the key to your config for future sessions:

config.json
{
  "mcpServers": {
    "nefesh": {
      "url": "https://mcp.nefesh.ai/mcp",
      "headers": {
        "X-Nefesh-Key": "nfsh_free_..."
      }
    }
  }
}

Option B: Get a key first, then connect

Sign up at nefesh.ai/signup (1,000 calls/month, no credit card), then add the config with your key:

config.json
{
  "mcpServers": {
    "nefesh": {
      "url": "https://mcp.nefesh.ai/mcp",
      "headers": {
        "X-Nefesh-Key": "YOUR_API_KEY"
      }
    }
  }
}

Agent-specific config files

AgentConfig file
Cursor~/.cursor/mcp.json
Windsurf~/.codeium/windsurf/mcp_config.json
Claude Desktop~/Library/Application Support/Claude/claude_desktop_config.json
Claude Code.mcp.json (project root)
VS Code (Copilot).vscode/mcp.json or ~/Library/Application Support/Code/User/mcp.json
Clinecline_mcp_settings.json (via UI: "Configure MCP Servers")
Continue.dev.continue/config.yaml
Roo Code.roo/mcp.json
Kiro (Amazon)~/.kiro/mcp.json
OpenClaw~/.config/openclaw/mcp.json
JetBrains IDEsSettings > Tools > MCP Server
Zed~/.config/zed/settings.json (uses context_servers)
OpenAI Codex CLI~/.codex/config.toml
Goose CLI~/.config/goose/config.yaml
ChatGPT DesktopSettings > Apps > Add MCP Server (UI)
Gemini CLISettings (UI)
AugmentSettings Panel (UI)
ReplitIntegrations Page (web UI)
LibreChatlibrechat.yaml (self-hosted)
VS Code (Copilot) β€” uses servers instead of mcpServers
config.json
{
  "servers": {
    "nefesh": {
      "type": "http",
      "url": "https://mcp.nefesh.ai/mcp",
      "headers": {
        "X-Nefesh-Key": "<YOUR_API_KEY>"
      }
    }
  }
}
Zed β€” uses context_servers in settings.json
config.json
{
  "context_servers": {
    "nefesh": {
      "settings": {
        "url": "https://mcp.nefesh.ai/mcp",
        "headers": {
          "X-Nefesh-Key": "<YOUR_API_KEY>"
        }
      }
    }
  }
}
OpenAI Codex CLI β€” uses TOML in ~/.codex/config.toml
toml
[mcp_servers.nefesh]
url = "https://mcp.nefesh.ai/mcp"
Continue.dev β€” uses YAML in .continue/config.yaml
yaml
mcpServers:
  - name: nefesh
    type: streamable-http
    url: https://mcp.nefesh.ai/mcp

All agents connect via Streamable HTTP β€” no local installation required.

A2A Integration (Agent-to-Agent Protocol v1.0)

Nefesh is also available as an A2A-compatible agent. While MCP handles tool-calling (your agent calls Nefesh), A2A enables agent-collaboration β€” other AI agents can communicate with Nefesh as a peer.

Agent Card: /.well-known/agent-card.json

A2A Endpoint: POST https://mcp.nefesh.ai/a2a (JSON-RPC 2.0)

A2A SkillDescription
get-human-stateStress state (0-100), suggested_action, adaptation_effectiveness
ingest-signalsSend biometric signals, receive unified state
get-trigger-memoryPsychological trigger profile (active vs resolved)
get-session-historyTimestamped history with trend

Same authentication as MCP β€” X-Nefesh-Key header or Authorization: Bearer token. Free tier works on both protocols.

Source: nefesh-ai/nefesh-a2a Β· Docs: nefesh.ai/docs/a2a

MCP Tools

ToolAuthDescription
request_api_keyNoRequest a free API key. You MUST ask the user for their real email first. Do not invent or guess emails. The user receives a verification link they must click. Poll with check_api_key_status until ready.
check_api_key_statusNoPoll for API key activation using the same email the user provided. Returns pending or ready with API key.
get_human_stateYesGet stress state (0-100), suggested_action (maintain/simplify/de-escalate/pause), and adaptation_effectiveness β€” a closed-loop showing whether your previous action reduced stress.
ingestYesSend biometric signals (heart rate, HRV, voice tone, expression, sentiment, 30+ fields) and get unified state back. Include subject_id for trigger memory.
get_trigger_memoryYesGet psychological trigger profile β€” which topics cause stress (active) and which have been resolved over time.
get_session_historyYesGet timestamped state history with trend (rising/falling/stable).

How self-provisioning works

Your AI agent can get a free API key autonomously. You only click one email link.

  1. Agent asks you: "What is your email address?"
  2. Agent calls request_api_key(your_real_email). No API key needed for this call.
  3. You receive a verification email and click the link
  4. Agent polls check_api_key_status(your_real_email) every 10 seconds
  5. Once verified, the agent receives the API key and can use all other tools

Important: The agent must use your real, accessible email address. Disposable emails are blocked. The verification link must be clicked by you to activate the key.

Free tier: 1,000 calls/month, all signal types, 10 req/min. No credit card.

Quick test

After adding the config, ask your AI agent:

"What tools do you have from Nefesh?"

It should list the 6 tools above.

Pricing

PlanPriceAPI Calls
Free$01,000/month, no credit card
Solo$25/month50,000/month
EnterpriseCustomCustom SLA

CLI Alternative

Prefer the terminal over MCP? Use the Nefesh CLI (10-32x lower token cost than MCP for AI agents):

Terminal
npm install -g @nefesh/cli
nefesh ingest --session test --heart-rate 72 --tone calm
nefesh state test --json

GitHub: nefesh-ai/nefesh-cli

Gateway Alternative

Want the AI to adapt automatically? Use the Nefesh Cognitive Compute Router. Change your LLM base URL to gateway.nefesh.ai and the gateway adjusts system prompt and temperature based on biometric state. Three modes: OpenAI-compatible (/v1/chat/completions), Anthropic passthrough (/v1/messages), and Unified Anthropic for any backend. Zero code changes.

GitHub: nefesh-ai/nefesh-gateway

Human State Protocol (HSP)

Nefesh implements and maintains the Human State Protocol, an open specification for exchanging human physiological state between AI systems. HSP defines a standard JSON format for stress scores, behavioral recommendations, and adaptation feedback so any agent can produce or consume human state data interoperably. Apache 2.0.

GitHub: nefesh-ai/human-state-protocol Β· Docs: nefesh.ai/docs/hsp

Documentation

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

Last commit
5mo ago
Most recent push to the default branch.
Tools exposed
6
Callable tools this server registers over MCP.
Directory activity
1 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Nefesh β€” Real Time Human State Awareness for AI

Nefesh β€” Real-Time Human State Awareness for AI is a hosted MCP server. Add it as a remote server in your client's config: "mcpServers": { "nefesh-real-time-human-state-awareness-for-ai": { "url": "https://mcp.nefesh.ai/mcp" } }

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Technical Specs & Signals

CategoryπŸ’¬Communication
More technical detailsExpand β–Ύ
TransportSSE (Remote)
Last updatedApr 16, 2026
13/13 checks healthy over the last 46d
Views1
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars0
GitHub Star CountTotal stargazers on GitHub representing community popularity (0 stars).
Last commit5mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Apr 16, 2026
47Quality signal: Fair Β· 47/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools24/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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