ai-ml

nefesh-mcp-server

nefesh-ai

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.

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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 [email protected]"

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 62 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.662 reviews
  • Isabella Brown· Dec 28, 2024

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

  • Shikha Mishra· Dec 24, 2024

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

  • Mia Garcia· Dec 20, 2024

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

  • Isabella Thomas· Dec 16, 2024

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

  • Aarav Sanchez· Dec 16, 2024

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

  • Layla Huang· Dec 12, 2024

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

  • Omar Chen· Nov 19, 2024

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

  • Yash Thakker· Nov 15, 2024

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

  • Naina Martin· Nov 11, 2024

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

  • Yuki Shah· Nov 11, 2024

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

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