HN mention sentiment: praise, critique and reading intent

Audit sentiment toward each named book or entity in pasted JSON or CSV. Separate praise, critique, reading intent and neutral mentions before counting recommendations.

html· HTML resultOpen tool

Quick start

Call this tool from your code in three languages.

cURL

curl -X POST 'https://api.elysiatools.com/en/api/tools/hn-mention-sentiment-positive-negative-classifier' \
  -H 'Content-Type: application/json' \
  -d '{"mentions":"[\n  {\n    \"id\": \"praise\",\n    \"entity\": \"Dune\",\n    \"context\": \"I loved Dune.\",\n    \"commentId\": \"101\"\n  },\n  {\n    \"id\": \"critique\",\n    \"entity\": \"Dune\",\n    \"context\": \"I hated Dune.\",\n    \"commentId\": \"102\"\n  },\n  {\n    \"id\": \"reading\",\n    \"entity\": \"Dune\",\n    \"context\": \"I wanted to read Dune.\",\n    \"commentId\": \"103\"\n  }\n]","format":"json","mode":"rules","corrections":"","threshold":0.85}'

API reference

Send a POST request with your inputs as JSON. File parameters require a separate upload first.

Endpoint

HTTP

POST https://api.elysiatools.com/en/api/tools/hn-mention-sentiment-positive-negative-classifier

Parameters

Name	Type	Required	Description
mentions	textarea	Yes	—
format	select	No	—
mode	select	No	—
corrections	textarea	No	—
threshold	number	No	—

Response format

HTML result

JSON

{
  "result": "<div>Processed HTML content</div>",
  "error": "Error message (optional)",
  "message": "Notification message (optional)",
  "metadata": {
    "key": "value"
  }
}

MCP integration

Add this tool to your Model Context Protocol server so AI agents can list and call it.

Server configuration

Add this block to your MCP client configuration:

mcp.json

{
  "mcpServers": {
    "elysiatools-hn-mention-sentiment-positive-negative-classifier": {
      "name": "hn-mention-sentiment-positive-negative-classifier",
      "description": "Audit sentiment toward each named book or entity in pasted JSON or CSV. Separate praise, critique, reading intent and neutral mentions before counting recommendations.",
      "baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=hn-mention-sentiment-positive-negative-classifier",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

List available tools

After connecting to the SSE endpoint, list the exposed tools:

tools/list

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list"
}

Call this tool

Invoke the tool by its id, passing arguments built from its parameters:

tools/call

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "hn-mention-sentiment-positive-negative-classifier",
    "arguments": {
  "mentions": "[\n  {\n    \"id\": \"praise\",\n    \"entity\": \"Dune\",\n    \"context\": \"I loved Dune.\",\n    \"commentId\": \"101\"\n  },\n  {\n    \"id\": \"critique\",\n    \"entity\": \"Dune\",\n    \"context\": \"I hated Dune.\",\n    \"commentId\": \"102\"\n  },\n  {\n    \"id\": \"reading\",\n    \"entity\": \"Dune\",\n    \"context\": \"I wanted to read Dune.\",\n    \"commentId\": \"103\"\n  }\n]",
  "format": "json",
  "mode": "rules",
  "corrections": "",
  "threshold": 0.85
}
  }
}

Chain multiple tools in one session with a comma-separated toolId list, e.g. /mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp (max 20).

Questions or issues? Contact [email protected]