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Construisez des serveurs MCP authentifiés à partir de fichiers…

Échafaudez, construisez et exécutez des serveurs MCP à partir d'une structure de projet Python prévisible avec outils, prompts, ressources, authentification et hooks OpenTelemetry.

Qu'est-ce que Construisez des serveurs MCP authentifiés à partir de fichiers… ?

Golf est un framework qui simplifie la création d'applications serveur MCP (Model Context Protocol). Avec ce skill, vous pouvez définir les capacités de votre serveur (outils, prompts et ressources) sous forme de fichiers Python simples, organisés dans une structure de projet prévisible. Le framework gère l'authentification, la télémétrie avec OpenTelemetry et l'exécution du serveur, ce qui le rend idéal pour les développeurs qui souhaitent créer rapidement des serveurs MCP prêts pour la production. Il supporte les clients MCP compatibles et s'installe via pip. La CLI Golf permet de générer la structure du projet en une commande, puis vous ajoutez vos composants dans les répertoires dédiés. Le skill est référencé sur Agent Skill Exchange et sa documentation complète est disponible sur docs.golf.dev.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/build-authenticated-mcp-servers-from-python-files-with-golf

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Documentation

Construisez des serveurs MCP authentifiés à partir de fichiers Python avec Golf

Échafaudez, construisez et exécutez des serveurs MCP à partir d'une structure de projet Python prévisible avec outils, prompts, ressources, authentification et hooks OpenTelemetry.

Prérequis

Python 3.10+, pip, CLI Golf, client compatible MCP

Installation

Utilisez le chemin d'installation en amont ou la configuration correspondant à votre environnement :

  • pip install golf-mcp

Exigences et mises en garde de la source en amont :

  • Golf est un framework conçu pour simplifier la création d'applications serveur MCP. Il permet aux développeurs de définir les capacités du serveur — outils, prompts et ressources — sous forme de fichiers Python simples dans une structure conventionnelle...
  • Assurez-vous d'avoir Python (3.10+ recommandé) installé. Ensuite, installez Golf avec pip :
  • tools/, resources/, prompts/ : Contiennent vos fichiers Python, chacun définissant un seul composant. Ces répertoires peuvent également contenir des sous-répertoires imbriqués pour mieux organiser vos composants (par exemple, tools/payments...

Notes d'utilisation de base ou de démarrage :

Documentation

Source

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

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Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

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Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.

Add managed long-term memory to LangGraph agents with LangMem

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Add multimodal lifelong memory to MCP and Python agents with Sim

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Add persistent project memory and hook-driven synthesis with Cla

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Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

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Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Adobe Firefly API Batch Image Generator

Calls Adobe Firefly

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