L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Design et création · SKILL.md

grav-cms-mcp-connector

grav-cms-mcp-connector

MCP connector for Grav flat-file CMS – enables AI-powered content creation, theme management, and Markdown-based site management through Claude

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: grav-cms-mcp-connector description: "MCP connector for Grav flat-file CMS – enables AI-powered content creation, theme management, and Markdown-based site management through Claude" license: MIT tags: ["dev","cms","mcp","grav","markdown"] difficulty: intermediate time_to_master: "4-8 weeks" version: "1.0.0"

Grav Cms MCP Connector

Overview

This skill enables Claude to interact with Grav Cms through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Grav Cms's CLI/File-based, allowing natural language control of Grav Cms operations, intelligent automation, and AI-powered assistance for Grav Cms workflows.

When to Use This Skill

  • Page creation and frontmatter configuration
  • Theme and blueprint customization
  • Plugin configuration and management
  • Twig template generation with AI
  • Multi-language content management

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Grav Cms         │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (CLI/File-based)│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Grav Cms operations as tools Claude can invoke. The server translates natural language intentions into CLI/File-based calls.

Key Endpoints/Interfaces

grav pages, grav backup, grav install, grav cache, grav composer

Implementation

// Grav Cms MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "grav-cms-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Grav Cms resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Grav Cms CLI/File-based
    const response = await fetch(`${BASE_URL}grav pages`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Grav Cms",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}grav pages`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Grav Cms data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}grav pages`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "grav-cms-mcp-connector": {
      "command": "node",
      "args": ["path/to/grav-cms-mcp-connector/index.js"],
      "env": {
        "GRAV_CMS_API_KEY": "your-api-key",
        "GRAV_CMS_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

  1. Authentication: Store API keys securely using environment variables; never hardcode credentials
  2. Rate Limiting: Implement request throttling to respect Grav Cms API rate limits
  3. Error Handling: Provide clear, actionable error messages for common failure scenarios
  4. Pagination: Handle paginated responses for large datasets efficiently
  5. Caching: Cache frequently accessed read-only data to reduce API calls
  6. Security: Validate all inputs before passing to the Grav Cms API; sanitize outputs
  7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Create a documentation site structure with sidebar navigation and search functionality"

Security Considerations

  • All API credentials must be stored as environment variables
  • Implement input validation and sanitization for all tool parameters
  • Use HTTPS for all API communications
  • Follow the principle of least privilege for API token permissions
  • Audit log all write operations for compliance tracking

Resources

Changelog

VersionDateChanges
1.0.02026-04-01Initial MCP connector skill

Part of SkillGalaxy - 10,000+ comprehensive skills for AI-assisted development.

Skills enregistrées