score_intentScore mentions for buying-stage intent.

Most mentions are noise. The signal is the small fraction where someone is actively asking — "what's the best alternative to X", "we're evaluating Y, has anyone shipped with it?", "anyone running Z at scale?". score_intent takes a list of mentions and a category, and returns each one with a 0-100 intent score, the reason behind the score, and a suggested next action. Three credits per call, regardless of mention count up to 200.

When to call this tool

The classic agent shape is "give me leads my team should reach out to today, ranked." score_intent is the second link in that chain. The first link is scan_mentions; the third, optionally, is build_sequence on the top-scored authors. The category argument is what tunes the model — "developer-tools CRM" produces very different scores from "DTC supplements" against the same raw mention list.

The score is a calibrated probability that the post represents an active buying-stage moment, not a generic positive or negative sentiment read. A glowing tweet about your product is rarely high intent; a frustrated thread about a competitor's pricing usually is. The tool surfaces the reason — for the agent to defend the recommendation downstream.

Input schema

{
  "mentions": ["object  // shape from scan_mentions"],
  "category": "string  // your product category",
  "min_score": "integer  // optional, filter out below this",
  "include_action": "boolean  // default true"
}

Output schema

{
  "scored": [
    { "mention": { "…" }, "intent": 0-100, "reason": "…", "action": "…" }
  ],
  "summary": { "high": 12, "medium": 28, "low": 160 },
  "credits_used": 3
}

Example invocations

1. Claude Desktop

Find people on Reddit asking about CRM alternatives this
week. Score by intent. Send me the top 10.

2. ChatGPT custom GPT

Pipeline: scan_mentions → score_intent → output ranked list
with reasons. Always show min_score 60.

3. Cursor MCP

Use score_intent to filter mentions.json for buying intent
in "observability tools". Output as CSV.

4. n8n

{ "jsonrpc": "2.0", "method": "tools/call",
  "params": { "name": "score_intent",
    "arguments": { "mentions": "{{$json.mentions}}", "category": "sales engagement", "min_score": 70 } } }

5. Raw curl

curl -X POST https://www.mentionfox.com/mcp \
  -H "Authorization: Bearer $FOXAPIS_KEY" \
  -d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"score_intent","arguments":{"mentions":[],"category":"email API"}}}'

Sample output

{
  "summary": { "high": 7, "medium": 19, "low": 142 },
  "scored": [
    { "intent": 88,
      "reason": "Asking r/SaaS for a Postmark replacement, includes price ceiling.",
      "action": "Reply with case study + 14-day trial" }
  ],
  "credits_used": 3
}

Credit cost & rate limits

Flat 3 credits per call, up to 200 mentions. Lists over 200 should be batched. 60 calls per minute per key.

Error codes & recovery