---

name: mediawiki-mcp-connector description: "MCP connector for MediaWiki – enables AI-powered wiki content creation, page management, and knowledge base operations through Claude" license: GPL tags: ["dev","wiki","mcp","mediawiki","documentation"] difficulty: intermediate time_to_master: "4-8 weeks" version: "1.0.0"

Mediawiki MCP Connector

Overview

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

When to Use This Skill

• Wiki page creation and editing via natural language
• Category and template management
• Cross-page linking and navigation structuring
• User and permission management
• Content migration and import/export

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Mediawiki        │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (MediaWiki API )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Mediawiki operations as tools Claude can invoke. The server translates natural language intentions into MediaWiki API calls.

Key Endpoints/Interfaces

api.php?action=edit, api.php?action=query, api.php?action=parse, api.php?action=upload, api.php?action=move

Implementation

// Mediawiki 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: "mediawiki-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Mediawiki 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 Mediawiki MediaWiki API
    const response = await fetch(`${BASE_URL}api.php?action=edit`, {
      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 Mediawiki",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}api.php?action=edit`, {
      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 Mediawiki 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}api.php?action=edit`, {
      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": {
    "mediawiki-mcp-connector": {
      "command": "node",
      "args": ["path/to/mediawiki-mcp-connector/index.js"],
      "env": {
        "MEDIAWIKI_API_KEY": "your-api-key",
        "MEDIAWIKI_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 Mediawiki 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 Mediawiki API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Create a new wiki page for the API documentation with proper templates and category tags"

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

• Mediawiki Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

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