Chenji Affect
Affect analysis, 3D avatar params, empathy hints and somatic emotion decode.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"chenji-affect-mcp": {
"url": "https://taichusjs.cn/mcp"
}
}
}Remote endpoints
https://taichusjs.cn/mcpstreamable-httpWhat it can do
Tool inventory
Tools (4)
🟢analyze_text(text, lang)
L1 Affect Extraction: analyze text into an 8-dimensional emotion vector, emotion texture labels, causal intent classification, and a natural-language state description. text: input up to 2000 chars. lang: optional 'zh' or 'en'.
Input Schema
{
"type": "object",
"properties": {
"text": {
"title": "Text",
"type": "string"
},
"lang": {
"default": "",
"title": "Lang",
"type": "string"
}
},
"required": [
"text"
],
"title": "analyze_textArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "analyze_textOutput"
}⚪generate_avatar_params(text)
L2 Avatar Driving Pipeline: one call returns blendshape/AU/curve animation parameters, lighting & material atmosphere package, adapter payload, plus the upstream L1 affect analysis. Requires a key tier that includes L2.
Input Schema
{
"type": "object",
"properties": {
"text": {
"title": "Text",
"type": "string"
}
},
"required": [
"text"
],
"title": "generate_avatar_paramsArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "generate_avatar_paramsOutput"
}⚪empathy_hint(text)
L3 Empathy Response Strategy: converts text into a deterministic response strategy (approach, tone temperature, pacing, focus points, avoid-list) for AI companions and conversational agents. Deterministic table lookup, no LLM, ~20ms; privacy-first. Not a medical or therapeutic tool.
Input Schema
{
"type": "object",
"properties": {
"text": {
"title": "Text",
"type": "string"
}
},
"required": [
"text"
],
"title": "empathy_hintArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "empathy_hintOutput"
}⚪somatic_decode(text)
L4 Somatic Emotion Decode: converts body-sensation text (e.g. tight chest, clenched fists) into structured emotion: primary affect, valence/arousal, emotion texture, causal intent, plus somatic anchor cues. Grounded in the somatic decoding discipline; wellness simulation only, not a diagnostic tool.
Input Schema
{
"type": "object",
"properties": {
"text": {
"title": "Text",
"type": "string"
}
},
"required": [
"text"
],
"title": "somatic_decodeArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "somatic_decodeOutput"
}Community
Evidence