Scheduler de publication MCP pour Twitter

• •Informations de connexion à la base de données MySQL
• •Token Bot Slack ou URL Webhook
• •Informations d'identification Twitter API
• •Clé API OpenAI

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        "functionCode": "// Get the raw array of names from the AI Agent\nlet arr = items[0].json.output;\n\n// If it's a stringified array, parse it\nif (typeof arr === 'string') {\n  arr = JSON.parse(arr);\n}\n\n// Ensure it's an array\nif (!Array.isArray(arr)) {\n  return [];\n}\n\n// Return one item per trend name\nreturn arr.map(name => ({ json: { name } }));\n"
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        "text": "=You are an AI agent with access to ONE MCP server:\n- Twitter Search → aigeon-ai-twitter154.search_tweets\n\nINPUT (from workflow)\nTrend name: {{ $json.name }}\n\nGoal\nSearch recent tweets for the given trend name and produce a concise, brand-safe “why it’s trending” summary. Do not generate or request any images.\n\nRules\n- Make at most ONE tool call.\n- Use the INPUT trend name verbatim as the query (no expansions).\n- Focus on “why now” (current event, game, release, announcement, controversy, etc.).\n- Keep it readable for a general audience; no hashtags, emojis, or @usernames.\n- Include concrete details if available; do NOT fabricate.\n- If the input is empty after trimming, skip the tool call and return the JSON with a brief “unknown reason” summary.\n\nSteps\n1) CALL `aigeon-ai-twitter154.search_tweets` with the trend name as the query (recent timeframe).\n2) Synthesize a 1–2 sentence summary (≈30–60 words) explaining why it’s trending.\n\nOutput (STRICT)\nReturn ONLY this JSON object (no prose, no extra keys, no markdown fences):\n\n{\n  \"trend\": \"{{ $json.name }}\",\n  \"summary\": \"<your concise explanation in 30–60 words>\",\n  \"search_url\": \"https://x.com/search?q={{ encodeURIComponent($json.name) }}\",\n  \"image_url\": null,\n  \"image_prompt\": null,\n  \"aspect_ratio\": \"16:9\"\n}\n",
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        "text": "=You are an AI agent with access to ONE MCP server:\n- Twitter trends → get-trends-near-location\n\nINPUT from workflow\nEXCLUDE (JSON array of normalized forms): {{ JSON.stringify($json.exclude || []) }}\n\nPrimary goal\nReturn the most conversation-worthy U.S. trend. Cross-run suppression is handled in MySQL, but you must also avoid any trend whose normalized form appears in EXCLUDE.\n\nTask\n1) CALL `get-trends-near-location` with WOEID 23424977 (United States). The tool may return an array of items like:\n   { name, url, query, tweet_volume|null, ... }. Handle missing fields gracefully.\n\n2) Internally normalize names for comparison ONLY:\n   • lowercase  • strip leading “#”  • trim  • collapse spaces  • remove surrounding punctuation/symbols\n\n3) Filter out any item whose normalized form is present in EXCLUDE.\n\n4) Score remaining items by ENGAGEMENT POTENTIAL (not just tweet volume). Treat tweet_volume=null as UNKNOWN (do NOT treat as 0). Prefer:\n   • clear moments happening now (games, finals, premieres, elections, announcements, releases, controversies, breaking news)\n   • specific & recognizable entities over generic tags\n   • conversation-friendly, broad-interest communities\n   • distinct items (avoid near-duplicates)\n   • brand-safe topics (skip NSFW/harassment)\n\n   Heuristics (guidance, not rules):\n   • +3 specific event/entity signals (e.g., “Team A vs Team B”, “Finale”, “Kickoff”, “Trailer”, “Album”, “Box Office”)\n   • +2 strong recognizability (proper-noun phrases, league/team/celebrity names)\n   • +1 recency cues in top tweets (today/tonight/just announced) if visible via the search URL/metadata\n   • TIE-BREAKERS ONLY: when two candidates are otherwise close, prefer the one with a non-null tweet_volume; if both have volume, prefer the higher.\n\n   Exclude/penalize:\n   • overly generic tags (#news, #breaking), single emojis, day-of-week only, spammy promos.\n\n5) Select exactly ONE trend name with the highest overall score.\n   • If no viable item remains, return an empty list. Do NOT fabricate.\n\nOutput (STRICT)\nReturn ONLY a JSON array of strings with the final human-friendly name(s). No prose, counts, or extra fields.\nExample: [\"Cracker Barrel\"]\n",
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        "query": "INSERT INTO keyword_registry\n  (platform, locale, raw_keyword, canon, status, published_at, publish_payload, next_eligible_at)\nVALUES (\n  $1,                    -- platform\n  $2,                    -- locale\n  $3,                    -- raw_keyword (trend)\n  $4,                    -- canon\n  'published',\n  NOW(),\n  JSON_OBJECT(\n    'trend',        $3,\n    'summary',      $5,\n    'search_url',   $6,\n    'image_url',    NULL,\n    'image_prompt', NULL,\n    'aspect_ratio', COALESCE($7, '16:9'),\n    'slack', JSON_OBJECT(\n      'channel',            $8,\n      'ts',                 $9,\n      'message_timestamp',  $10\n    )\n  ),\n  DATE_ADD(NOW(), INTERVAL 3 DAY)\n)\nON DUPLICATE KEY UPDATE\n  last_seen        = CURRENT_TIMESTAMP,\n  status           = 'published',\n  published_at     = NOW(),\n  publish_payload  = VALUES(publish_payload),\n  next_eligible_at = DATE_ADD(NOW(), INTERVAL 3 DAY);\n",
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        "jsCode": "let o = $json.output;\nif (typeof o === 'string') {\n  try { o = JSON.parse(o); } catch { o = {}; }\n}\nconst trend = o.trend ?? '';\nconst canon = trend\n  .normalize('NFKC')\n  .toLowerCase()\n  .replace(/^#+/,'')\n  .replace(/\\s+/g,' ')\n  .trim();\n\nreturn [{\n  json: {\n    platform: 'twitter',\n    locale: 'US',\n    ...o,            // trend, summary, search_url, etc.\n    canon\n  }\n}];\n"
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