IndieML Substance API

io.github.ezchxv0.1.14更新于 Oct 1, 2026

Scores text for substance, depth, and clarity in under 25ms.

已验证Streamable HTTP可网页运行AI & MLData & AnalyticsKnowledge & Memory

概览

AI 生成的概览

通过托管 API 对文本的实质内容、深度和清晰度打分,让助手快速排序或筛选内容。

功能
IndieML 的 Substance API 会评估一段文本,返回实质内容、深度和清晰度方面的分数,据称耗时低于 25 毫秒。它是一个托管的、基于 transformer 的评分服务,定位为快速且低成本的前置筛选环节,而非完整的大语言模型。该 MCP 服务器通过 Streamable HTTP 向助手提供这一评分接口,同一服务也可通过 REST、Python SDK 以及面向 stdio 客户端的 npx 桥接方式访问。
适用场景
适合助手需要低成本地分诊或排序大量文本的场景:在向量化或建立索引之前预筛选文档、按信息密度对信息流和档案排序,或依据深度而非关键词或情感来分流支持工单、反馈和用户生成内容。
运行要求
通过 Streamable HTTP 访问 indieml.app 上的远程端点,并提供 IndieML API 密钥,放在 X-Api-Key 请求头中。对于仅支持 stdio 的客户端(如 Claude Desktop),npx mcp-remote 桥接需要 Node.js v20 或更高版本,并通过 INDIEML_API_KEY 环境变量提供密钥。Python SDK 需要 Python 3.11 或更高版本。需要网络访问。
安装前请注意
该服务为远程服务,提交评分的文本会发送给第三方,请避免发送机密或受监管的内容。需要 API 密钥,通过 X-Api-Key(桥接方式为 INDIEML_API_KEY)传递,应作为机密保存,不要写入共享配置。README 未说明价格、速率限制或数据保留策略,在生产流程中依赖它之前应先确认这些信息。

安装

在 SourceWeft 中

  1. 打开 控制台中的 IndieML Substance API,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "indieml": {
      "type": "http",
      "url": "https://indieml.app/mcp/"
    }
  }
}

README

IndieML - Substance API

A lightweight, transformer-based API that evaluates text for substance, depth, and clarity in under 25ms while achieving 87% of the accuracy of a full-scale LLM. This package provides REST API, Python SDK, and MCP configuration details to natively connect the hosted Substance API to a wide variety of applications. For more information, including API key requests, please visit https://indieml.app.

REST API

For integrations outside the Python and MCP ecosystems (such as standard OpenAI function calling, custom AI scripts, or raw HTTP requests), the hosted endpoint accepts standard JSON payload requests.

cURL

bash
curl -X POST https://indieml.app/v1/score/substance \  -H "X-API-Key: YOUR_API_KEY_HERE" \  -H "Content-Type: application/json" \  -d '{"input_text": "This is a test run."}'

Python (Requests)

python
import requests
response = requests.post(    "https://indieml.app/v1/score/substance",    headers={"X-API-Key": "YOUR_API_KEY_HERE"},    json={"input_text": "This is a test run."})print(response.json())

Node.js (Fetch)

javascript
const response = await fetch("https://indieml.app/v1/score/substance", {  method: "POST",  headers: {    "X-API-Key": "YOUR_API_KEY_HERE",    "Content-Type": "application/json"  },  body: JSON.stringify({ input_text: "This is a test run." })});console.log(await response.json());

Python SDK

Installation (requires Python 3.11 or higher)

bash
pip install indieml

Usage

python
from indieml import Substance
# Initialize the APIapi = Substance(api_key="YOUR_API_KEY_HERE")
# Evaluate text for substance, depth, and clarityresult = api.score("This is a test run.")print(result)

MCP Server Integration

You can integrate the Substance API into AI coding assistants by routing them to our cloud-native ASGI endpoints.

Cursor, ChatGPT, and Claude Code (Streamable HTTP)

For agents that support native Streamable HTTP / SSE configurations, provide the remote URL and authorization header directly:

json
{  "mcpServers": {    "indieml": {      "type": "http",      "url": "https://indieml.app/mcp/",      "headers": {        "X-API-Key": "YOUR_API_KEY_HERE"      }    }  }}

Claude Desktop (stdio via npx Bridge)

For local Linux, Mac, and Windows clients that require stdio transport, use the npx mcp-remote bridge to route the connection to the cloud endpoint. Node.js v20+ is required. Ensure your OS environment variables include INDIEML_API_KEY.

json
{  "mcpServers": {    "indieml": {      "command": "npx",      "args": [        "-y",        "mcp-remote",        "https://indieml.app/mcp/",        "--header",        "X-Api-Key:${INDIEML_API_KEY}"      ],      "env": {        "INDIEML_API_KEY": "YOUR_API_KEY_HERE"      }    }  }}

Applications & Use Cases

The Substance API is a fast and inexpensive pre-screening tool designed to integrate with your existing classification pipeline.

While it evaluates substance across any text, it is uniquely suited for high-volume, automated data pipelines:

1. RAG & AI Knowledge-Base Preprocessing

Pre-filter text before expensive LLM vectorization and processing.

  • Knowledge Portals: Filter out bloated, low-signal documentation prior to indexing.
  • Document Archives: Rank corporate or research archives based on actual information density.
  • Feed Aggregation: Rank news, newsletters, or educational content by structural depth rather than just recency or click-through rates.

2. Automated Content Triage & Large-Scale Screening

Route inbound text based on substance, clarity, and depth rather than relying solely on keywords or sentiment analysis.

  • Support Tickets: Identify highly detailed bug reports and instantly route them past basic classification bots to human operators.
  • Customer Feedback: Isolate high-quality product critiques from generic "it's great" or "it's broken" noise.
  • User-Generated Content: Surface high-effort, high-value forum posts and survey responses while automatically burying low-effort spam.

来源:README.md,提交 15658e5

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版本历史

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  1. v0.1.14最新Oct 1, 2026