About Chenji Affect
Chenji Affect is an MCP server powered by the Chenji Affect API that turns text into structured affect signals. It serves MCP clients needing emotion states, avatar animation parameters, or empathy strategies, and also decodes text about bodily sensations.
Key features
- 01Produce an eight-dimensional affect vector
- 02Extract emotion texture and intent
- 03Generate 3D avatar driving parameters
- 04Provide empathy response strategies
- 05Decode body-sensation text
Use cases
- 01Convert text into structured affect states
- 02Generate animation and atmosphere parameters for a 3D avatar
- 03Derive an empathy response strategy from text
- 04Identify emotions from descriptions of bodily sensations
MCP support
It exposes tools to MCP clients for text affect analysis, avatar parameter generation, empathy hints, and somatic decoding.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://taichusjs.cn/mcpclaude mcp add --transport http zhiyu-dev-chenji-affect https://taichusjs.cn/mcp{"mcpServers":{"zhiyu-dev-chenji-affect":{"type":"http","url":"https://taichusjs.cn/mcp"}}}Open in Cursor{"servers":{"zhiyu-dev-chenji-affect":{"type":"http","url":"https://taichusjs.cn/mcp"}}}Open in VS Code{"mcpServers":{"zhiyu-dev-chenji-affect":{"type":"http","url":"https://taichusjs.cn/mcp"}}}{"mcpServers":{"zhiyu-dev-chenji-affect":{"serverUrl":"https://taichusjs.cn/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
https://taichusjs.cn/mcpAuth requiredTools
4| Parameter | Type | Required | Description |
|---|---|---|---|
| lang | string | No | |
| text | string | Yes |
Details
From the project READMEChenji Affect MCP Server
Turn raw text into structured affect signals — an 8-dimensional emotion vector, emotion texture labels, causal intent, 3D avatar driving parameters, empathy response strategies, and somatic-sensation emotion decoding — inside any MCP-capable client (Claude Desktop, Cursor, Qoder, etc.).
Powered by the Chenji Affect API (taichusjs.cn).
Tools
| Tool | Level | Description |
|---|---|---|
analyze_text | L1 | Text → 8-dim affect vector + emotion texture + intent + state description |
generate_avatar_params | L2 | Text → blendshape/AU/curve animation package + lighting/atmosphere package + upstream L1 |
empathy_hint | L3 | Text → deterministic empathy response strategy (approach, tone, pacing, focus, avoid-list), ~20ms, no LLM |
somatic_decode | L4 | Body-sensation text → structured emotion (primary affect, valence/arousal, texture, intent) + somatic anchors |
Remote mode (no install)
Clients that support streamable-http MCP can connect directly, no local package needed:
{
"mcpServers": {
"chenji-affect": {
"type": "streamable-http",
"url": "https://taichusjs.cn/mcp",
"headers": { "x-api-key": "cj-your-key-here" }
}
}
}
Install
Requires Python 3.10+.
pip install "mcp>=1.0,<2"
Get an API key:
- Free BASIC plan (100 req/day, 3,000/month): subscribe on RapidAPI, or request a trial key at
https://taichusjs.cn/trial.html.
Configure (Claude Desktop example)
{
"mcpServers": {
"chenji-affect": {
"command": "python",
"args": ["/absolute/path/to/server.py"],
"env": {
"CHENJI_API_KEY": "cj-your-key-here"
}
}
}
}
Environment variables:
| Variable | Required | Description |
|---|---|---|
CHENJI_API_KEY | Yes | Your Chenji API key |
CHENJI_API_BASE | No | Defaults to https://taichusjs.cn |
Notes
- L1 input is limited to 2000 characters (every 200 chars = 1 billing unit).
generate_avatar_paramsrequires a key tier that includes L2.empathy_hintandsomatic_decodeare billed as L1-family calls (trial tier applies).- Privacy: raw text is never persisted by the API; only key hashes and telemetry are retained.
- Wellness and affect-state simulation only — not a medical or diagnostic tool.
Links
- Website: https://taichusjs.cn
- RapidAPI listing: https://rapidapi.com/zhiyudev/api/chenji-affect-api
- Machine-readable capability card: https://taichusjs.cn/.well-known/capability.jsonld
Comments
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