IndieML Substance API

io.github.ezchxv0.1.14更新於 Oct 1, 2026

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

已驗證Streamable HTTP可網頁執行AI & MLData & AnalyticsKnowledge & Memory

概覽

AI 產生的概覽

透過託管 API 為文字的實質內容、深度與清晰度評分,讓助理快速排序或篩選內容。

功能
IndieML 的 Substance API 會評估一段文字,回傳實質內容、深度與清晰度的分數,據稱耗時低於 25 毫秒。它是一項託管、以 transformer 為基礎的評分服務,定位為快速且低成本的前置篩選步驟,而非完整的大型語言模型。這個 MCP 伺服器透過 Streamable HTTP 向助理提供此評分端點,同一服務也可透過 REST、Python SDK,以及供 stdio 用戶端使用的 npx 橋接方式存取。
適用情境
適合助理需要以低成本分流或排序大量文字的情境:在向量化或建立索引前預先篩選文件、依資訊密度排序動態消息與檔案庫,或依深度而非關鍵字或情緒來分流客服單、意見回饋與使用者產出內容。
執行需求
透過 Streamable HTTP 存取 indieml.app 上的遠端端點,並提供 IndieML API 金鑰,放在 X-Api-Key 標頭中。對於僅支援 stdio 的用戶端(例如 Claude Desktop),npx mcp-remote 橋接需要 Node.js v20 或以上版本,並透過 INDIEML_API_KEY 環境變數提供金鑰。Python SDK 需要 Python 3.11 或以上版本。需要網路存取。
安裝前請注意
此服務為遠端服務,送出評分的文字會傳送給第三方,請避免傳送機密或受監管的內容。需要 API 金鑰,透過 X-Api-Key(橋接方式為 INDIEML_API_KEY)傳遞,應以機密方式保存,不要寫入共用設定。README 未說明價格、速率限制或資料保留政策,在正式流程中依賴它之前應先確認這些資訊。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 IndieML Substance API,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "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.

來源:README.md,提交 15658e5

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版本歷史

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  1. v0.1.14最新Oct 1, 2026