ai-ml

nefesh-mcp-server

by nefesh-ai

Real-time human state awareness for AI agents via Model Context Protocol (MCP)

Provides AI agents with real-time awareness of human physiological and emotional state through biometric data analysis, including closed-loop feedback on adaptation effectiveness.

github stars

0

best for

  • / General purpose MCP workflows

capabilities

  • / request_api_key
  • / check_api_key_status
  • / get_human_state
  • / ingest
  • / get_trigger_memory
  • / get_session_history

what it does

Provides AI agents with real-time awareness of human physiological and emotional state through biometric data analysis, including closed-loop feedback on adaptation effectiveness.

about

nefesh-mcp-server is a community-built MCP server published by nefesh-ai that provides AI assistants with tools and capabilities via the Model Context Protocol. Real-time human state awareness for AI agents via Model Context Protocol (MCP) It is categorized under ai ml. This server exposes 6 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install nefesh-mcp-server in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

nefesh-mcp-server is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

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 — 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:

{
  "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 — your agent will get one automatically.

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

Then ask your agent:

"Connect to Nefesh and get me a free API key for name@example.com"

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

{
  "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:

{
  "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)
<details> <summary><strong>VS Code (Copilot)</strong> — uses <code>servers</code> instead of <code>mcpServers</code></summary>
{
  "servers": {
    "nefesh": {
      "type": "http",
      "url": "https://mcp.nefesh.ai/mcp",
      "headers": {
        "X-Nefesh-Key": "<YOUR_API_KEY>"
      }
    }
  }
}
</details> <details> <summary><strong>Zed</strong> — uses <code>context_servers</code> in settings.json</summary>
{
  "context_servers": {
    "nefesh": {
      "settings": {
        "url": "https://mcp.nefesh.ai/mcp",
        "headers": {
          "X-Nefesh-Key": "<YOUR_API_KEY>"
        }
      }
    }
  }
}
</details> <details> <summary><strong>OpenAI Codex CLI</strong> — uses TOML in <code>~/.codex/config.toml</code></summary>
[mcp_servers.nefesh]
url = "https://mcp.nefesh.ai/mcp"
</details> <details> <summary><strong>Continue.dev</strong> — uses YAML in <code>.continue/config.yaml</code></summary>
mcpServers:
  - name: nefesh
    type: streamable-http
    url: https://mcp.nefesh.ai/mcp
</details>

All agents connect via Streamable HTTP — 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 — 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 — X-Nefesh-Key header or Authorization: Bearer token. Free tier works on both protocols.

MCP Tools

ToolAuthDescription
request_api_keyNoRequest a free API key by email. Poll with check_api_key_status until ready.
check_api_key_statusNoPoll for API key activation. Returns pending or ready with API key.
get_human_stateYesGet stress state (0-100), suggested_action (maintain/simplify/de-escalate/pause), and adaptation_effectiveness — 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 — 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 calls request_api_key(email) — no API key needed for this call
  2. You receive a verification email and click the link
  3. Agent polls check_api_key_status(request_id) every 10 seconds
  4. Once verified, the agent receives the API key and can use all other tools

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):

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

GitHub: nefesh-ai/nefesh-cli

Documentation

Privacy

  • No video or audio uploads — edge processing runs client-side
  • No PII stored
  • GDPR/BIPA compliant — cascading deletion via delete_subject
  • Not a medical device — for contextual AI adaptation only

License

MIT — see LICENSE.

FAQ

What is the nefesh-mcp-server MCP server?
nefesh-mcp-server is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for nefesh-mcp-server?
This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
MCP server reviews

Ratings

4.510 reviews
  • Shikha Mishra· Oct 10, 2024

    nefesh-mcp-server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Piyush G· Sep 9, 2024

    We evaluated nefesh-mcp-server against two servers with overlapping tools; this profile had the clearer scope statement.

  • Chaitanya Patil· Aug 8, 2024

    Useful MCP listing: nefesh-mcp-server is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Sakshi Patil· Jul 7, 2024

    nefesh-mcp-server reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Ganesh Mohane· Jun 6, 2024

    I recommend nefesh-mcp-server for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Oshnikdeep· May 5, 2024

    Strong directory entry: nefesh-mcp-server surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Dhruvi Jain· Apr 4, 2024

    nefesh-mcp-server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Rahul Santra· Mar 3, 2024

    According to our notes, nefesh-mcp-server benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Pratham Ware· Feb 2, 2024

    We wired nefesh-mcp-server into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Yash Thakker· Jan 1, 2024

    nefesh-mcp-server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.