Confluence MCP Server

A Model Context Protocol (MCP) server that provides Confluence search functionality for LexisNexis internal systems:

1. Confluence Search - Search and retrieve content from LexisNexis Confluence

Overview

This MCP server is built using the @modelcontextprotocol/sdk and provides seamless integration with LexisNexis Confluence through standardized MCP protocols. It enables AI assistants and other MCP clients to search Confluence documentation.

Related MCP server: Confluence MCP Server

Features

🔍 Confluence Search Tool

• Search across LexisNexis Confluence spaces, pages, blog posts, and attachments

• Retrieve detailed content from search results

• Support for CQL (Confluence Query Language) queries

• Configurable search result limits

• Bearer token authentication

Prerequisites

• Node.js 18+

• npm or pnpm package manager

• TypeScript 5.3+

• Access to LexisNexis internal networks (for Confluence services)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd confluence_mcp

2. Install dependencies:

   npm install
   # or
   pnpm install

3. Build the project:

   npm run build

Configuration

Environment Variables

Create a .env file in the project root with the following variables:

# Confluence Configuration
CONFLUENCE_BEARER_TOKEN=your_confluence_bearer_token_here
LIMIT=5  # Optional: Number of search results to return (default: 2)

Bearer Token Setup

To use the Confluence search functionality, you'll need a valid bearer token:

1. Log into LexisNexis Confluence

2. Generate an API token

3. Set the CONFLUENCE_BEARER_TOKEN environment variable

Usage

Starting the Server

npm start

The server runs on stdio transport and will output:

Confluence MCP Server running on stdio

Tool Usage

Confluence Search

Tool Name: confluence-search

Description: Search Confluence using a search string. Input must start with 'confluence' (case-insensitive) followed by the search string.

Input Format:

confluence <your search terms>

Examples:

confluence API documentation
confluence deployment guide
confluence troubleshooting

Response: Returns the full content of matching Confluence pages, including HTML markup.

Technical Architecture

MCP Protocol Implementation

This server implements the Model Context Protocol (MCP) specification:

• Transport Layer: Uses stdio transport for communication

• Message Format: JSON-RPC 2.0 protocol

• Tool Registration: Dynamic tool registration with schema validation

• Error Handling: Structured error responses with proper HTTP status codes

Request/Response Flow

1. Client Request: MCP client sends tool invocation request

2. Validation: Input parameters validated using Zod schemas

3. Processing: Tool-specific logic executed (API calls, authentication)

4. Response: Structured response returned with content array

Rate Limiting & Performance

• Confluence API: Built-in 1-second delay between content detail requests

• Concurrent Requests: Handles multiple tool invocations safely

• Memory Management: Streaming responses for large content retrieval

API Endpoints

Confluence API

• Base URL: https://confluence.lexisnexis.dev

• Search Endpoint: /rest/api/search

• Content Endpoint: /rest/api/content/{id}

Project Structure

confluence_mcp/
├── src/
│   └── confluence.ts          # Main MCP server implementation
├── build/                     # Compiled JavaScript output
│   └── confluence.js         # Confluence search functionality
├── package.json              # Project dependencies and scripts
├── tsconfig.json             # TypeScript configuration
├── jest.config.js            # Jest testing configuration
└── manifest.xml              # Office add-in manifest (legacy)

Development

Building

npm run build

This compiles TypeScript files from src/ to build/ directory.

Testing

npm test

Note: Tests are currently not implemented (returns "Error: no test specified").

Code Structure

The server uses the MCP SDK to:

1. Create an MCP Server instance for Confluence search functionality

2. Register tools using server.tool() method

3. Handle stdio transport for communication

4. Implement async tool handlers with proper error handling

Key Dependencies

• @modelcontextprotocol/sdk - Core MCP functionality

• node-fetch - HTTP requests to APIs

• zod - Runtime type validation and schema validation

Advanced Configuration

Confluence Search Customization

You can customize the search behavior by modifying the CQL query in src/confluence.ts:

const cqlQuery = `siteSearch ~ "${searchString}" AND type in ("space","user","page","blogpost")`;

Environment-Specific Settings

Different environments may require different configurations:

# Development
CONFLUENCE_BEARER_TOKEN=dev_token_here
LIMIT=2

# Production  
CONFLUENCE_BEARER_TOKEN=prod_token_here
LIMIT=10

Error Handling

The server implements comprehensive error handling:

• Network Errors: Automatic retry logic for transient failures

• Authentication Errors: Clear error messages for invalid credentials

• Rate Limiting: Graceful handling of API rate limits

• Input Validation: Schema-based validation with detailed error messages

Security Considerations

⚠️ Important Security Notes:

1. Credentials: Never commit bearer tokens or passwords to version control

2. Environment Variables: Use .env files or secure environment variable management

3. Network Access: This server requires access to internal LexisNexis networks

4. Token Handling: Session tokens should be handled securely and not logged

Troubleshooting

Common Issues

1. "Invalid input" errors:

   • Ensure Confluence queries start with "confluence"

2. Authentication failures:

   • Check bearer token validity for Confluence

   • Ensure network connectivity to LexisNexis services

3. No search results:

   • Try broader search terms

   • Check if you have access to the Confluence spaces

   • Verify the LIMIT environment variable is set appropriately

4. Rate limiting errors:

   • The server implements 1-second delays between requests

   • For high-volume usage, consider implementing exponential backoff

   • Monitor API rate limits in your environment

5. Memory issues with large responses:

   • Reduce the LIMIT environment variable

   • Filter search results more specifically

   • Consider implementing response streaming for very large content

Debug Logging

The server outputs debug information to stderr:

• Search queries and bearer tokens

• API response statuses

• Error messages and stack traces

Enable verbose logging by setting:

export DEBUG=confluence-mcp:*

Performance Monitoring

Monitor key metrics:

• Response times for Confluence searches

• Memory usage during large content retrieval

• Error rates for search operations

Integration Examples

Using with MCP Clients

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "confluence-mcp": {
      "command": "node",
      "args": ["c:/dev/confluence_mcp/build/confluence.js"],
      "env": {
        "CONFLUENCE_BEARER_TOKEN": "your_token_here",
        "LIMIT": "5"
      }
    }
  }
}

Custom MCP Client

import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js';

const transport = new StdioClientTransport({
  command: 'node',
  args: ['./build/confluence.js']
});

const client = new Client({
  name: "confluence-client",
  version: "1.0.0"
}, {
  capabilities: {}
});

await client.connect(transport);

License

ISC License

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests (when test framework is implemented)

5. Submit a pull request

Changelog

Version 1.0.0

• Initial release with Confluence search tool

• MCP SDK integration

• TypeScript implementation

• Basic error handling and logging

For more information about the Model Context Protocol, visit: https://modelcontextprotocol.io/