A Model Context Protocol and Agent-to-Agent (A2A) server that gives AI agents real-time awareness of human physiological state.
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.
Related MCP server: WAVE MCP Server
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.
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:
"Get me a free Nefesh API key using my email address"
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_..."
}
}
}
}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 | Config file |
Cursor |
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Windsurf |
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Claude Desktop |
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Claude Code |
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VS Code (Copilot) |
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Cline |
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Continue.dev |
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Roo Code |
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Kiro (Amazon) |
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OpenClaw |
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JetBrains IDEs | Settings > Tools > MCP Server |
Zed |
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OpenAI Codex CLI |
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Goose CLI |
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ChatGPT Desktop | Settings > Apps > Add MCP Server (UI) |
Gemini CLI | Settings (UI) |
Augment | Settings Panel (UI) |
Replit | Integrations Page (web UI) |
LibreChat |
|
{
"servers": {
"nefesh": {
"type": "http",
"url": "https://mcp.nefesh.ai/mcp",
"headers": {
"X-Nefesh-Key": "<YOUR_API_KEY>"
}
}
}
}{
"context_servers": {
"nefesh": {
"settings": {
"url": "https://mcp.nefesh.ai/mcp",
"headers": {
"X-Nefesh-Key": "<YOUR_API_KEY>"
}
}
}
}
}[mcp_servers.nefesh]
url = "https://mcp.nefesh.ai/mcp"mcpServers:
- name: nefesh
type: streamable-http
url: https://mcp.nefesh.ai/mcpAll agents connect via Streamable HTTP — no local installation required.
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 Skill | Description |
| Stress state (0-100), suggested_action, adaptation_effectiveness |
| Send biometric signals, receive unified state |
| Psychological trigger profile (active vs resolved) |
| Timestamped history with trend |
Same authentication as MCP — X-Nefesh-Key header or Authorization: Bearer token. Free tier works on both protocols.
Source: nefesh-ai/nefesh-a2a · Docs: nefesh.ai/docs/a2a
Tool | Auth | Description |
| No | 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 |
| No | Poll for API key activation using the same email the user provided. Returns |
| Yes | Get stress state (0-100), |
| Yes | Send biometric signals (heart rate, HRV, voice tone, expression, sentiment, 30+ fields) and get unified state back. Include |
| Yes | Get psychological trigger profile — which topics cause stress (active) and which have been resolved over time. |
| Yes | Get timestamped state history with trend (rising/falling/stable). |
Your AI agent can get a free API key autonomously. You only click one email link.
Agent asks you: "What is your email address?"
Agent calls request_api_key(your_real_email). No API key needed for this call.
You receive a verification email and click the link
Agent polls check_api_key_status(your_real_email) every 10 seconds
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.
After adding the config, ask your AI agent:
"What tools do you have from Nefesh?"
It should list the 6 tools above.
Plan | Price | API Calls |
Free | $0 | 1,000/month, no credit card |
Solo | $25/month | 50,000/month |
Enterprise | Custom | Custom SLA |
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 --jsonGitHub: nefesh-ai/nefesh-cli
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
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 · Docs: nefesh.ai/docs/hsp
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
MIT — see LICENSE.
Check the status of a pending API key request.
Use the exact same email the user provided to request_api_key.
Poll this every 10 seconds. Once the user clicks the verification
link in their inbox, status changes from 'pending' to 'ready'
and the response includes the API key.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden and discloses polling behavior, status transition from 'pending' to 'ready', and that response includes API key when ready. Missing error handling details (e.g., invalid email), but adds significant behavioral context.
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?
Three sentences with purpose first, followed by usage guidelines and behavioral details. Every sentence contributes meaning, no redundant or missing parts.
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?
For a simple polling tool with one parameter and no output schema, description covers essential flow: polling interval, status transition, and key retrieval. Lacks error handling for invalid requests, but overall adequate.
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?
Only one parameter 'email' with 0% schema description coverage. Description adds value by stating to use the same email as in request_api_key, which is crucial for correct usage.
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?
Description states 'Check the status of a pending API key request' with a specific verb and resource. It explicitly references the sibling tool 'request_api_key', distinguishing its role as a polling companion for status checking.
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?
Clear context provided: use same email from request_api_key, poll every 10 seconds, and expect status change after user clicks verification link. No explicit exclusions, but the usage flow is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get current unified human state for a session. Call this before generating important responses.
Returns:
- state: calm | relaxed | focused | stressed | acute_stress
- stress_score: 0-100 (lower = calmer)
- confidence: 0.0-1.0 (based on signal quality and device type)
- suggested_action: maintain_engagement | simplify_and_focus | de-escalate_and_shorten | pause_and_ground
- action_reason: human-readable explanation of why this action was suggested
- adaptation_effectiveness (on 2nd+ call): shows whether your previous suggested_action actually reduced stress — contains previous_action, stress_delta, and effective boolean. Use this to self-improve.
Use suggested_action to adapt your response: calm/relaxed = full complexity, focused = shorter and structured, stressed = max 2 sentences, acute_stress = one grounding sentence only.
Requires a prior ingest call to have data. Not a medical device.
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses return fields, the precondition of prior ingest, adaptive usage of suggested_action, that adaptation_effectiveness appears on 2nd+ call, and a 'not a medical device' disclaimer. This is comprehensive transparency.
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?
The description is well-structured: a one-sentence purpose, a clear list of return fields, adaptation guidance, and a prerequisite/disclaimer. It is information-dense without being bloated.
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?
For a tool with no output schema, the description fully documents the return structure and semantics, usage guidance, and prerequisites. It is remarkably complete.
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?
The only parameter session_id is barely explained beyond the schema title. The description says 'for a session' and implies ingest must have happened, but doesn't define its format, origin, or how it relates to ingest. With schema description coverage at 0%, this is a significant gap.
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?
The description clearly states a specific action: 'Get current unified human state for a session.' It also specifies when to use it ('Call this before generating important responses'), which distinguishes it from siblings like ingest or get_session_history.
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?
It provides explicit context: call before important responses, and requires a prior ingest call. However, it does not mention alternatives or when not to use, so it's not a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get state history for a session over time.
Returns timestamped datapoints with stress_score, state, and heart_rate for each observation.
Includes an overall trend: rising | falling | stable.
Use minutes parameter to control the lookback window (default: 5, max: 60).
Useful for detecting stress patterns during a conversation. Not a medical device.
| Name | Required | Description | Default |
|---|---|---|---|
| minutes | No | ||
| session_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden and does well: it discloses return fields (stress_score, state, heart_rate), the trend format (rising | falling | stable), the minutes parameter's range, and a disclaimer. It doesn't mention side effects or permissions, but as a 'get' operation this is minor. Adds value beyond the schema's scope.
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?
The description is efficiently packed: purpose, return data, trend, parameter control, use case, and disclaimer—all in a few sentences. No fluff, and key information is front-loaded.
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?
Even without annotations or an output schema, the description provides a comprehensive picture: what it does, what it returns (including field names and trend values), how to control the lookback window, and its intended use case. It's complete for a relatively simple read tool.
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?
The description explains the minutes parameter with its default (5) and adds a max (60) not present in the schema, which is helpful. However, session_id is only implied ('for a session') and not explicitly defined, though it's a required identifier. With 0% schema description coverage, the description partially compensates but could be more explicit.
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?
The description clearly states a specific verb and resource: 'Get state history for a session over time.' It distinguishes from siblings like get_human_state (likely current state) and get_trigger_memory by focusing on historical timestamped data with a trend.
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?
It provides a clear use case: 'Useful for detecting stress patterns during a conversation.' It doesn't explicitly name alternatives, but the 'over time' and 'trend' language strongly implies a historical analysis tool compared to likely current-state siblings. A brief statement about when not to use it would push this to a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve psychological trigger profile for a subject.
Returns which conversation topics consistently cause stress (active triggers) and which have been resolved over time.
- active triggers: topics where stress was elevated across multiple sessions. Tread carefully.
- resolved triggers: topics where stress has decreased. Safe to explore deeper.
Each trigger includes observation_count, avg_score, peak_score, and last_seen.
Requires prior ingest calls with the same subject_id. Not a medical device.
| Name | Required | Description | Default |
|---|---|---|---|
| subject_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It discloses that the tool returns behavioral profiles (active vs resolved), the fields included, and the prerequisite of prior ingest calls. It also adds a safety disclaimer ('Not a medical device'). It does not mention side effects, but 'Retrieve' implies read-only.
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?
The description is moderately long but well-structured with bullet points and clear labels. It covers purpose, output details, and prerequisites without excessive verbosity. Slightly more concise could be better, but the structure aids readability.
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?
Given one parameter and no output schema, the description compensates well by detailing what is returned (active vs resolved triggers, observation_count, avg_score, peak_score, last_seen). It also notes the ingest prerequisite. It is reasonably complete for a simple retrieval tool.
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?
The schema provides only the parameter name (subject_id) with 0% description coverage. The description adds that prior ingest calls must use the same subject_id, giving some context. However, it does not explain what the subject_id represents or where to obtain it, leaving partial ambiguity.
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?
The description uses a specific verb ('Retrieve') and identifies the exact resource ('psychological trigger profile'). It clearly differentiates from siblings by focusing on trigger memory, not general session history or human state. The active/resolved distinction adds precision.
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?
The description provides clear context: it requires prior ingest calls with the same subject_id, implying use after ingest. It also warns to tread carefully with active triggers. However, it does not explicitly mention alternatives or when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Send biometric signals from any sensor, get unified state back.
Required: session_id + timestamp (ISO 8601) + at least one signal.
Send whatever you have — the API fuses all signals into one state.
Common signals (highest impact):
- heart_rate (bpm, 30-220) + rmssd (ms) — cardiovascular
- tone: calm | tense | anxious | hostile — vocal
- sentiment: -1.0 to 1.0 — textual
- expression: relaxed | neutral | tense — visual
For trigger memory (cross-session psychological tracking):
- Include subject_id (consistent per user, hashed)
- Include user_message + ai_response to detect stress topics
Returns same fields as get_human_state plus signals_received list and topics_detected.
source_device is optional but improves confidence scoring. Not a medical device.
| Name | Required | Description | Default |
|---|---|---|---|
| eda | No | ||
| gaze | No | ||
| sdnn | No | ||
| spo2 | No | ||
| tone | No | ||
| pnn50 | No | ||
| rmssd | No | ||
| posture | No | ||
| urgency | No | ||
| mean_ibi | No | ||
| ibi_count | No | ||
| sentiment | No | ||
| timestamp | Yes | ||
| confidence | No | ||
| engagement | No | ||
| expression | No | ||
| heart_rate | No | ||
| session_id | Yes | ||
| subject_id | No | ||
| ai_response | No | ||
| sleep_stage | No | ||
| speech_rate | No | ||
| stress_score | No | ||
| user_message | No | ||
| glucose_mg_dl | No | ||
| glucose_trend | No | ||
| source_device | No | ||
| activity_level | No | ||
| cognitive_load | No | ||
| eeg_beta_power | No | ||
| glucose_mmol_l | No | ||
| eeg_alpha_power | No | ||
| eeg_theta_power | No | ||
| respiratory_rate | No | ||
| skin_temperature | No | ||
| pitch_variability | No | ||
| steps_last_minute | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must cover behavioral traits. It explains signal fusion, return format (same as get_human_state plus signals_received and topics_detected), and that source_device improves confidence. However, it omits details like idempotency, persistence, or error behavior.
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?
The description is well-structured: a one-line summary, required fields, common signals, cross-session use case, return format, and a note about source_device. Every sentence adds value relative to the 37-parameter complexity.
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?
Despite no output schema and 37 parameters, the description covers the core functionality, required inputs, and most impactful signals. It references get_human_state for return fields, which may suffice if that tool is documented. Less common parameters are not explained, but the description is reasonably complete for its purpose.
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?
Schema description coverage is 0%, but the description adds significant meaning: it lists common signals with ranges (e.g., heart_rate 30-220), groups them by type (cardiovascular, vocal, etc.), and explains the purpose of subject_id, user_message, and ai_response for trigger memory. This fully compensates for the missing schema descriptions.
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?
The description clearly states the tool's action ('Send biometric signals') and outcome ('get unified state back'), distinguishing it from sibling tools that retrieve or query data. The verb and resource are specific.
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?
The description specifies required fields (session_id, timestamp, at least one signal) and provides guidance for cross-session tracking (include subject_id, user_message, ai_response). It implies when to use this tool vs. siblings (others read, this writes), but does not explicitly state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Request a free Nefesh API key. No existing key needed.
IMPORTANT: You MUST ask the user for their real email address before
calling this tool. Do NOT invent, guess, or generate an email address.
The user will receive a verification link they must click to activate
the key. Without clicking that link, no API key will be issued.
Disposable or temporary email services are blocked.
Example prompt to the user: "What is your email address? You will
receive a verification link to activate your free API key."
Flow: call this with the user's real email, then poll
check_api_key_status every 10 seconds until status is 'ready'.
Free tier: 1,000 API calls/month, no credit card required.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses blocking of disposable emails, need for user to click verification link, polling pattern, and rate limit. No annotations exist, so description fully carries the burden.
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?
Well-organized with warnings, example, and flow. Every sentence adds value; no redundancy.
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?
Covers purpose, usage, behavioral quirks, and post-call steps. Simple tool with no output schema; description is fully complete.
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?
Schema coverage is 0%, but description explains the single parameter 'email' well: must be real user email, cannot be disposable. Lacks validation details but sufficient for usage.
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?
Clear verb+resource ('Request ... API key') and distinguishes from sibling tools like check_api_key_status. States no existing key needed.
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?
Explicit instructions: ask user for real email, do not invent, provide example prompt, describe verification link and polling workflow. Covers when and how to use.
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.
v2.1.0check_api_key_statusget_human_stateget_session_historyget_trigger_memoryingestrequest_api_keyget_human_stateget_session_historyget_trigger_memoryingestget_human_stateget_session_historyget_trigger_memoryingestScored across 6 tools
Each tool has a clear distinct role: ingest submits data, get_human_state reads current state, get_trigger_memory and get_session_history cover long-term and recent context, and the two API key tools handle onboarding. Even where get_human_state and ingest both return state-like data, their write/read purposes are explicitly separated by the descriptions.
Most tools follow a verb_noun snake_case pattern (get_human_state, get_trigger_memory, get_session_history, request_api_key, check_api_key_status). The one outlier is 'ingest', which is a bare verb rather than verb_noun, but it is still lowercase and style-consistent. This is a minor deviation rather than a mixed-convention problem.
Six tools is a well-scoped count for a specialized server. Two focus on API key access and four cover the core state/ingest/history/memory workflows, with no redundant helpers or unnecessary bulk. The count feels right for both the domain and the agents that would consume it.
The core workflow is covered: send signals, check current human state, pull session history, and retrieve trigger memory. The primary gaps are not fatal—there is no way to enumerate known sessions/subjects or revoke an API key—but those are auxiliary management features rather than dead ends in the primary state-tracking workflow.
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