This workflow follows the Agent → Discord recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"name": "recherche-stage",
"nodes": [
{
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "// Fusionner les donn\u00e9es Tavily avec les scores MCP\nconst searchData = $('Search').item.json;\nconst mcpData = $input.item.json;\n\nreturn {\n query: searchData.query,\n results: searchData.results,\n city_score: mcpData\n};"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
512,
0
],
"id": "dad4f474-f4f9-498f-a24d-de9491eaf2f1",
"name": "Fusion"
},
{
"parameters": {
"query": "=internship {{ $json.technology }} in {{ $json.city }}",
"options": {
"max_results": 5
}
},
"type": "@tavily/n8n-nodes-tavily.tavily",
"typeVersion": 1,
"position": [
128,
0
],
"id": "aec1211e-adf5-4c48-8e9d-b0411b24c32c",
"name": "Search",
"credentials": {
"tavilyApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"url": "=http://mcp-scorer:8080/find_city_score?city={{ $json.query.split(' in ')[1] }}",
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.3,
"position": [
320,
0
],
"id": "1566058a-472d-4492-8c8f-6f227e98b363",
"name": "MCP"
},
{
"parameters": {
"jsCode": "// Make an array of all combinaisons with the technologies and \n\nconst cities = [\n {\"city\": \"Berlin\"},\n {\"city\": \"Barcelona\"},\n {\"city\": \"Amsterdam\"}\n];\n\nconst technology = [\n {\"technology\": \"devops\"},\n {\"technology\": \"software engineer\"}\n];\n\nconst combinations = cities.flatMap(cityObj => {\n return technology.map(techObj => ({\n city: cityObj.city,\n technology: techObj.technology\n }));\n});\n\nreturn combinations;"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-80,
0
],
"id": "e85a4e15-8c2f-43ea-a7fc-1989a9dee7f7",
"name": "Return combinations of cities and technology matrix"
},
{
"parameters": {},
"type": "n8n-nodes-base.manualTrigger",
"typeVersion": 1,
"position": [
-304,
0
],
"id": "40a9ace1-a9b1-4d7b-918d-61acdd120271",
"name": "When clicking \u2018Execute workflow\u2019"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatMistralCloud",
"typeVersion": 1,
"position": [
736,
224
],
"id": "58ec7cb4-daae-438b-9d89-5201a8eea98f",
"name": "Mistral Cloud Chat Model",
"credentials": {
"mistralCloudApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"binaryPropertyName": "rapport",
"options": {
"fileName": "=stages_{{ $now.format('yyyy-MM-dd') }}.csv"
}
},
"type": "n8n-nodes-base.convertToFile",
"typeVersion": 1.1,
"position": [
1744,
416
],
"id": "d8020a44-d70d-4213-8142-7c8adc79d89b",
"name": "Convert to File"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatMistralCloud",
"typeVersion": 1,
"position": [
1760,
208
],
"id": "b0de2c42-befa-44db-a564-d57be78f80fb",
"name": "Mistral Cloud Chat Model1",
"credentials": {
"mistralCloudApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"authentication": "webhook",
"content": "={{ $json.output }}",
"options": {},
"files": {
"values": [
{
"inputFieldName": "rapport"
}
]
}
},
"type": "n8n-nodes-base.discord",
"typeVersion": 2,
"position": [
2384,
16
],
"id": "3a0c7f9b-e0ed-45f7-b1a5-7569444ea05e",
"name": "Discord",
"credentials": {
"discordWebhookApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"aggregate": "aggregateAllItemData",
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
1536,
0
],
"id": "8a0d90ed-2e56-49d6-968b-f9151bf52a96",
"name": "Aggregate"
},
{
"parameters": {
"promptType": "define",
"text": "=Tu es un assistant expert en recrutement.\n\nTA T\u00c2CHE :\nAnalyse les offres ci-dessous pour la ville concern\u00e9e.\nSCORE DE LA VILLE : {{ $json.city_score }} / 10\n\nR\u00c8GLES STRICTES :\n1. Retourne UNIQUEMENT un JSON valide.\n2. Pour CHAQUE offre, ajoutes-y le champ \"city_score\" avec la valeur {{ $json.city_score }} (ne l'invente pas, recopie-le).\n3. Format de sortie :\n[{\n \"company\": \"Nom\",\n \"title\": \"Titre\",\n \"location\": \"Ville\",\n \"city_score\": {{ $json.city_score }},\n \"src_url\": \"URL\",\n \"deadline\": \"Date ou Non sp\u00e9cifi\u00e9\",\n \"skills\": [\"Comp\u00e9tence 1\"],\n \"summary\": \"R\u00e9sum\u00e9 en 1 phrase\"\n}]\n\n4. IMPORTANT : Pas de markdown (```json), juste le JSON brut.\n\nDONN\u00c9ES \u00c0 ANALYSER :\n{{ JSON.stringify($json) }}",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
736,
0
],
"id": "7795a8eb-8977-4cad-a425-7a7cd9cfe482",
"name": "Analyse"
},
{
"parameters": {
"promptType": "define",
"text": "=Tu es un analyste Data pour le recrutement.\nVoici un tableau JSON complet des offres de stage trouv\u00e9es (avec un \"personal_score\" calcul\u00e9) :\n{{ JSON.stringify($json) }}\n\nG\u00e9n\u00e8re un rapport de synth\u00e8se structur\u00e9 et professionnel en suivant strictement ce plan :\n\n### 1. \ud83d\udcca Vue d'ensemble\n* **Nombre total d'offres pertinentes :** [Nombre]\n* **Villes repr\u00e9sent\u00e9es :** [Ex: Berlin (5), Amsterdam (3)]\n* **Fourchettes identifi\u00e9es :** [Ex: Salaire moyen observ\u00e9 / Dur\u00e9es fr\u00e9quentes]\n\n### 2. \ud83c\udfc6 Top 3 Recommandations (Bas\u00e9 sur le score personnel)\n*S\u00e9lectionne les 3 offres avec le meilleur 'personal_score' ou les plus prestigieuses.*\n1. **[Titre] chez [Entreprise]** ([Ville]) - Score: [Score]\n * *Pourquoi ce choix :* [Justification courte]\n2. ...\n3. ...\n\n### 3. \ud83d\udca1 Analyse & Conseils\n* **Tendance du march\u00e9 :** [Ex: Beaucoup de demande en DevOps...]\n* **Conseil strat\u00e9gique :** [Un conseil pour postuler]\n\nGarde un ton professionnel mais encourageant. Utilise des \u00e9mojis pour la lisibilit\u00e9.\n\nIMPORTANT : La r\u00e9ponse DOIT faire MOINS DE 1800 CARACT\u00c8RES (pour Discord).\nSois concis, utilise des listes \u00e0 puces (bullets) et va droit au but. Pas de blabla inutile.",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
1760,
0
],
"id": "3086aa6c-e74b-4d6a-a8db-ebfd31915a2a",
"name": "Synth\u00e8se"
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "llama3.2:1b",
"mode": "list",
"cachedResultName": "llama3.2:1b"
},
"messages": {
"values": [
{
"content": "=Tu es un assistant expert en recrutement et analyse d'offres d'emploi.\n\nTA T\u00c2CHE :\nAnalyse le contenu brut des r\u00e9sultats de recherche fournis ci-dessous (format JSON) et extrais les informations cl\u00e9s pour chaque offre de stage pertinente.\n\nR\u00c8GLES STRICTES :\n1. Tu dois retourner UNIQUEMENT un objet JSON valide.\n2. Si une information est manquante, utilise \"Non sp\u00e9cifi\u00e9\".\n3. Le format de sortie doit \u00eatre exactement celui-ci :\n[{\n \"company\": \"Nom de l'entreprise\",\n \"title\": \"Intitul\u00e9 du poste\",\n \"location\": \"Ville/Pays\",\n \"src_url\": \"L'URL d'origine\",\n \"deadline\": \"Date limite ou 'Non sp\u00e9cifi\u00e9'\",\n \"salary\": \"Salaire/Gratification ou 'Non sp\u00e9cifi\u00e9'\",\n \"duration\": \"Dur\u00e9e du stage ou 'Non sp\u00e9cifi\u00e9'\",\n \"skills\": [\"Comp\u00e9tence 1\"],\n \"summary\": \"R\u00e9sum\u00e9 en 1 phrase\"\n}]\n\n4. Si l'input contient plusieurs r\u00e9sultats, retourne un tableau d'objets : [...]\n5. IMPORTANT : Ne mets JAMAIS de backticks (```) ou de balises markdown. Donne uniquement le JSON brut.\n\nVOICI LES DONN\u00c9ES :\n{{ JSON.stringify($json) }}"
}
]
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.ollama",
"typeVersion": 1,
"position": [
-176,
448
],
"id": "a6a918dc-e7a1-4a23-897c-74d83d391442",
"name": "Analyse2",
"credentials": {
"ollamaApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "llama3.2:1b",
"mode": "list",
"cachedResultName": "llama3.2:1b"
},
"messages": {
"values": [
{
"content": "=Tu es mon assistant de recherche de stage.\nVoici la liste finale des offres retenues :\n{{ JSON.stringify($json) }}\n\nFais-moi une synth\u00e8se rapide :\n1. Combien d'offres as-tu trouv\u00e9es ?\n2. Quelle est la MEILLEURE offre selon toi et pourquoi ?\n3. Donne-moi un conseil cl\u00e9 pour r\u00e9ussir ma candidature \u00e0 cette entreprise."
}
]
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.ollama",
"typeVersion": 1,
"position": [
224,
448
],
"id": "708bda41-beb9-4713-876c-10b179a3162c",
"name": "Synthese2",
"credentials": {
"ollamaApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"content": "## Mod\u00e8les Ollama \nVous pouvez aussi utiliser un LLM qui tourne en local (si jamais vous avez d\u00e9pass\u00e9 le nombre de tokens par exemple). \n\nPour t\u00e9l\u00e9chargez un mod\u00e8le : \n```bash\ndocker exec ollama ollama pull llama3.2:1b\n```",
"height": 416,
"width": 976
},
"type": "n8n-nodes-base.stickyNote",
"position": [
-320,
240
],
"typeVersion": 1,
"id": "491bc6c6-d711-431f-8708-036c075a7658",
"name": "Sticky Note"
},
{
"parameters": {
"jsCode": "const results = [];\n\n// 1. Parsing du JSON\nfor (const item of $input.all()) {\n try {\n let content = item.json.output || item.json.message?.content || \"\";\n content = content.replace(/```json/g, \"\").replace(/```/g, \"\").trim();\n \n const parsed = JSON.parse(content);\n\n if (Array.isArray(parsed)) {\n results.push(...parsed);\n } else {\n results.push(parsed);\n }\n } catch (error) {\n }\n}\n\n// 2. BONUS : Calcul du Score Personnalis\u00e9\nresults.forEach(offer => {\n if (offer.city_score) {\n const qol = offer.city_score.quality_of_life_index || 0;\n const safety = offer.city_score.safety_index || 0;\n const climate = offer.city_score.climate_index || 0;\n\n const score = (qol * 0.5) + (safety * 0.3) + (climate * 0.2);\n\n offer.personal_score = parseFloat(score.toFixed(2));\n } else {\n offer.personal_score = 0;\n }\n});\n\nreturn results;"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1072,
0
],
"id": "9f111e0d-bb22-4b8a-b851-6bdf5853e5ff",
"name": "Formattage"
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 3
},
"conditions": [
{
"id": "00a39bac-8d03-4b87-9a7d-a1dfbbccdf02",
"leftValue": "={{ $json.personal_score }}",
"rightValue": 100,
"operator": {
"type": "number",
"operation": "gt"
}
}
],
"combinator": "and"
},
"options": {}
},
"type": "n8n-nodes-base.filter",
"typeVersion": 2.3,
"position": [
1280,
0
],
"id": "11b06c7c-ccd8-4c2d-85ae-8c76cbf9d0fc",
"name": "Filtres"
},
{
"parameters": {
"mode": "combine",
"combineBy": "combineByPosition",
"options": {}
},
"type": "n8n-nodes-base.merge",
"typeVersion": 3.2,
"position": [
2144,
16
],
"id": "d88ce249-f9ac-4cae-b63e-a911afdfd001",
"name": "Merge"
}
],
"connections": {
"Fusion": {
"main": [
[
{
"node": "Analyse",
"type": "main",
"index": 0
}
]
]
},
"Search": {
"main": [
[
{
"node": "MCP",
"type": "main",
"index": 0
}
]
]
},
"MCP": {
"main": [
[
{
"node": "Fusion",
"type": "main",
"index": 0
}
]
]
},
"Return combinations of cities and technology matrix": {
"main": [
[
{
"node": "Search",
"type": "main",
"index": 0
}
]
]
},
"When clicking \u2018Execute workflow\u2019": {
"main": [
[
{
"node": "Return combinations of cities and technology matrix",
"type": "main",
"index": 0
}
]
]
},
"Mistral Cloud Chat Model": {
"ai_languageModel": [
[
{
"node": "Analyse",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Mistral Cloud Chat Model1": {
"ai_languageModel": [
[
{
"node": "Synth\u00e8se",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Aggregate": {
"main": [
[
{
"node": "Synth\u00e8se",
"type": "main",
"index": 0
}
]
]
},
"Analyse": {
"main": [
[
{
"node": "Formattage",
"type": "main",
"index": 0
}
]
]
},
"Synth\u00e8se": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 0
}
]
]
},
"Formattage": {
"main": [
[
{
"node": "Filtres",
"type": "main",
"index": 0
}
]
]
},
"Filtres": {
"main": [
[
{
"node": "Aggregate",
"type": "main",
"index": 0
},
{
"node": "Convert to File",
"type": "main",
"index": 0
}
]
]
},
"Convert to File": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 1
}
]
]
},
"Merge": {
"main": [
[
{
"node": "Discord",
"type": "main",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1",
"availableInMCP": false
},
"versionId": "cc60ebaf-a2bd-45a7-bd42-f778431f74ee",
"meta": {
"templateCredsSetupCompleted": true
},
"id": "C7wJhwtzndzvDniSRdzkx",
"tags": []
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
discordWebhookApimistralCloudApiollamaApitavilyApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
recherche-stage. Uses @tavily/n8n-nodes-tavily, httpRequest, lmChatMistralCloud, discord. Event-driven trigger; 18 nodes.
Source: https://github.com/obitw/recherche-stage/blob/6371770c34496f0a7f7d8ec712ec727b9bbce3c7/rendu/recherche-stage.json — original creator credit. Request a take-down →
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