productivity

Atlassian Confluence

aashari

by aashari

Integrate Jira with Atlassian Confluence to list, retrieve, and search spaces and pages, plus convert content to Markdow

Integrates with Atlassian Confluence to provide direct access to spaces, pages, and content with tools for listing, retrieving, and searching using CQL while converting content to Markdown format

github stars

50

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Token-optimized TOON format reduces API costs by 30-60%Natural language queries to your knowledge base

best for

  • / Developers accessing technical documentation and API guides
  • / Product managers searching requirements and project specs
  • / Support teams finding troubleshooting guides
  • / HR teams accessing policy documents quickly

capabilities

  • / Search pages across Confluence spaces using CQL queries
  • / Read and retrieve Confluence pages with content converted to Markdown
  • / Create new pages and documentation in specified spaces
  • / Update existing Confluence pages and content
  • / Delete Confluence resources and pages
  • / List spaces and browse Confluence structure

what it does

Connects AI assistants to your Atlassian Confluence workspace so you can search, read, and manage pages and documentation using natural language.

about

Atlassian Confluence is a community-built MCP server published by aashari that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate Jira with Atlassian Confluence to list, retrieve, and search spaces and pages, plus convert content to Markdow It is categorized under productivity. This server exposes 5 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install Atlassian Confluence in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport. This server supports remote connections over HTTP, so no local installation is required.

license

MIT

Atlassian Confluence is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Connect AI to Your Confluence Knowledge Base

Transform how you access and interact with your team's knowledge by connecting Claude, Cursor AI, and other AI assistants directly to your Confluence spaces, pages, and documentation. Get instant answers from your knowledge base, search across all your spaces, and streamline your documentation workflow.

NPM Version

What You Can Do

  • Ask AI about your documentation: "What's our API authentication process?"
  • Search across all spaces: "Find all pages about security best practices"
  • Get instant answers: "Show me the latest release notes from the Product space"
  • Access team knowledge: "What are our HR policies for remote work?"
  • Review page comments: "Show me the discussion on the architecture document"
  • Create and update content: "Create a new page in the DEV space"

Perfect For

  • Developers who need quick access to technical documentation and API guides
  • Product Managers searching for requirements, specs, and project updates
  • HR Teams accessing policy documents and employee resources quickly
  • Support Teams finding troubleshooting guides and knowledge base articles
  • Anyone who wants to interact with Confluence using natural language

Quick Start

Get up and running in 2 minutes:

1. Get Your Confluence Credentials

Generate a Confluence API Token:

  1. Go to Atlassian API Tokens
  2. Click Create API token
  3. Give it a name like "AI Assistant"
  4. Copy the generated token immediately (you won't see it again!)

2. Try It Instantly

# Set your credentials
export ATLASSIAN_SITE_NAME="your-company"  # for your-company.atlassian.net
export ATLASSIAN_USER_EMAIL="[email protected]"
export ATLASSIAN_API_TOKEN="your_api_token"

# List your Confluence spaces (TOON format by default)
npx -y @aashari/mcp-server-atlassian-confluence get --path "/wiki/api/v2/spaces"

# Get details about a specific space with field filtering
npx -y @aashari/mcp-server-atlassian-confluence get \
  --path "/wiki/api/v2/spaces/123456" \
  --jq "{id: id, key: key, name: name, type: type}"

# Get a page with JMESPath filtering
npx -y @aashari/mcp-server-atlassian-confluence get \
  --path "/wiki/api/v2/pages/789" \
  --jq "{id: id, title: title, status: status}"

# Search for pages (using CQL)
npx -y @aashari/mcp-server-atlassian-confluence get \
  --path "/wiki/rest/api/search" \
  --query-params '{"cql": "type=page AND space=DEV"}'

Connect to AI Assistants

For Claude Desktop Users

Add this to your Claude configuration file (~/.claude/claude_desktop_config.json):

{
  "mcpServers": {
    "confluence": {
      "command": "npx",
      "args": ["-y", "@aashari/mcp-server-atlassian-confluence"],
      "env": {
        "ATLASSIAN_SITE_NAME": "your-company",
        "ATLASSIAN_USER_EMAIL": "[email protected]",
        "ATLASSIAN_API_TOKEN": "your_api_token"
      }
    }
  }
}

Restart Claude Desktop, and you'll see the confluence server in the status bar.

For Other AI Assistants

Most AI assistants support MCP (Cursor AI, Continue.dev, and others). Install the server globally:

npm install -g @aashari/mcp-server-atlassian-confluence

Then configure your AI assistant to use the MCP server with STDIO transport. The binary is available as mcp-atlassian-confluence after global installation.

Alternative: Configuration File

Create ~/.mcp/configs.json for system-wide configuration:

{
  "confluence": {
    "environments": {
      "ATLASSIAN_SITE_NAME": "your-company",
      "ATLASSIAN_USER_EMAIL": "[email protected]",
      "ATLASSIAN_API_TOKEN": "your_api_token"
    }
  }
}

Alternative config keys: The system also accepts "atlassian-confluence", "@aashari/mcp-server-atlassian-confluence", or "mcp-server-atlassian-confluence" instead of "confluence".

Using Environment Variables

You can also configure credentials using environment variables or a .env file:

# Create a .env file in your project directory
cat > .env << EOF
ATLASSIAN_SITE_NAME=your-company
[email protected]
ATLASSIAN_API_TOKEN=your_api_token
DEBUG=false
EOF

The server will automatically load these values from:

  1. Environment variables
  2. .env file in the current directory
  3. ~/.mcp/configs.json (as shown above)

Available Tools

This MCP server provides 5 generic tools that can access any Confluence API endpoint:

ToolDescription
conf_getGET any Confluence API endpoint (read data)
conf_postPOST to any endpoint (create resources)
conf_putPUT to any endpoint (replace resources)
conf_patchPATCH to any endpoint (partial updates)
conf_deleteDELETE from any endpoint (remove resources)

Tool Parameters

All tools share these common parameters:

  • path (required): The API endpoint path (e.g., /wiki/api/v2/spaces)
  • queryParams (optional): Query parameters as key-value pairs (e.g., {"limit": "25", "space-id": "123"})
  • jq (optional): JMESPath expression to filter/transform the response (e.g., results[*].{id: id, title: title})
  • outputFormat (optional): Output format - "toon" (default, 30-60% fewer tokens) or "json"

Tools that accept a request body (conf_post, conf_put, conf_patch):

  • body (required): Request body as a JSON object

Common API Paths

Spaces:

  • /wiki/api/v2/spaces - List all spaces
  • /wiki/api/v2/spaces/{id} - Get space details

Pages:

  • /wiki/api/v2/pages - List pages (use space-id query param to filter)
  • /wiki/api/v2/pages/{id} - Get page details
  • /wiki/api/v2/pages/{id}/body - Get page body (use body-format param)
  • /wiki/api/v2/pages/{id}/children - Get child pages
  • /wiki/api/v2/pages/{id}/labels - Get page labels

Comments:

  • /wiki/api/v2/pages/{id}/footer-comments - List/add footer comments
  • /wiki/api/v2/pages/{id}/inline-comments - List/add inline comments
  • /wiki/api/v2/footer-comments/{comment-id} - Get/update/delete comment

Blog Posts:

  • /wiki/api/v2/blogposts - List blog posts
  • /wiki/api/v2/blogposts/{id} - Get blog post

Search:

  • /wiki/rest/api/search - Search content (use cql query param)

TOON Output Format

What is TOON? TOON (Token-Oriented Object Notation) is a format optimized for LLM token efficiency, reducing token costs by 30-60% compared to JSON. It's the default output format for all tools.

Benefits:

  • Tabular arrays use fewer tokens than JSON arrays
  • Minimal syntax overhead (no quotes, brackets, commas where unnecessary)
  • Still human-readable and parseable

When to use JSON instead:

  • When you need standard JSON for other tools
  • When debugging or manual inspection is needed

Example comparison:

// JSON format (verbose)
{"results": [{"id": "123", "title": "My Page"}, {"id": "456", "title": "Other Page"}]}

// TOON format (efficient)
results:
  - id: 123
    title: My Page
  - id: 456
    title: Other Page

To use JSON instead of TOON, set outputFormat: "json" in your request.

JMESPath Filtering

All tools support optional JMESPath (jq) filtering to extract specific data and reduce token costs:

# Get just space names and keys
npx -y @aashari/mcp-server-atlassian-confluence get \
  --path "/wiki/api/v2/spaces" \
  --jq "results[].{id: id, key: key, name: name}"

# Get page title and status
npx -y @aashari/mcp-server-atlassian-confluence get \
  --path "/wiki/api/v2/pages/123456" \
  --jq "{id: id, title: title, status: status}"

IMPORTANT: Always use the jq parameter to filter responses to only the fields you need. Unfiltered responses can be very large and expensive in token costs.

JMESPath Syntax Reference:

  • Official docs: jmespath.org
  • Common patterns:
    • results[*] - All items in results array
    • results[0] - First item only
    • results[*].id - Just IDs from all items
    • results[*].{id: id, title: title} - Create objects with selected fields
    • results[?status=='current'] - Filter by condition

Real-World Examples

Explore Your Knowledge Base

Ask your AI assistant:

  • "List all the spaces in our Confluence"
  • "Show me details about the Engineering space"
  • "What pages are in our Product space?"
  • "Find the latest pages in the Marketing space"

Search and Find Information

Ask your AI assistant:

  • "Search for pages about API authentication"
  • "Find all documentation with 'security' in the title"
  • "Show me pages labeled with 'getting-started'"
  • "Search for content in the DEV space about deployment"

Access Specific Content

Ask your AI assistant:

  • "Get the content of the API Authentication Guide page"
  • "Show me the onboarding checklist document"
  • "What's in our security policies page?"
  • "Display the latest release notes"

Create and Update Content

Ask your AI assistant:

  • "Create a new page in the DEV space titled 'API Guide'"
  • "Add a comment to the architecture document"
  • "Update the page content with the new release info"

CLI Commands

The CLI mirrors the MCP tools for direct terminal access. All commands support the same parameters as the tools.

Available Commands

  • get - GET any Confluence endpoint
  • post - POST to any endpoint
  • put - PUT to any endpoint
  • patch - PATCH any endpoint
  • delete - DELETE from any endpoint

CLI Parameters

All commands:

  • -p, --path <path> (required) - API endpoint path
  • -q, --query-params <json> (optional) - Query parameters as JSON
  • --jq <expression> (optional) - JMESPath filter expression
  • -o, --output-format <format> (optional) - Output format: toon (def

FAQ

What is the Atlassian Confluence MCP server?
Atlassian Confluence is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Atlassian Confluence?
This profile displays 26 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

List & Promote Your MCP Server

Share your MCP server with the developer community

GET_STARTED →
MCP server reviews

Ratings

4.526 reviews
  • Nia Li· Nov 23, 2024

    We wired Atlassian Confluence into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Liam Khanna· Oct 14, 2024

    According to our notes, Atlassian Confluence benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Kofi Robinson· Sep 17, 2024

    Atlassian Confluence has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Sakshi Patil· Sep 13, 2024

    Strong directory entry: Atlassian Confluence surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Rahul Santra· Sep 9, 2024

    According to our notes, Atlassian Confluence benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Anika Robinson· Sep 1, 2024

    Atlassian Confluence reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Pratham Ware· Aug 28, 2024

    We wired Atlassian Confluence into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Olivia Tandon· Aug 20, 2024

    I recommend Atlassian Confluence for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Kofi Choi· Aug 8, 2024

    We evaluated Atlassian Confluence against two servers with overlapping tools; this profile had the clearer scope statement.

  • Chaitanya Patil· Aug 4, 2024

    Atlassian Confluence is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

showing 1-10 of 26

1 / 3