Atlassian Confluence▌
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
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
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.
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:
- Go to Atlassian API Tokens
- Click Create API token
- Give it a name like "AI Assistant"
- 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:
- Environment variables
.envfile in the current directory~/.mcp/configs.json(as shown above)
Available Tools
This MCP server provides 5 generic tools that can access any Confluence API endpoint:
| Tool | Description |
|---|---|
conf_get | GET any Confluence API endpoint (read data) |
conf_post | POST to any endpoint (create resources) |
conf_put | PUT to any endpoint (replace resources) |
conf_patch | PATCH to any endpoint (partial updates) |
conf_delete | DELETE 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 (usespace-idquery param to filter)/wiki/api/v2/pages/{id}- Get page details/wiki/api/v2/pages/{id}/body- Get page body (usebody-formatparam)/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 (usecqlquery 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 arrayresults[0]- First item onlyresults[*].id- Just IDs from all itemsresults[*].{id: id, title: title}- Create objects with selected fieldsresults[?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 endpointpost- POST to any endpointput- PUT to any endpointpatch- PATCH any endpointdelete- 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.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 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.
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Ratings
4.5★★★★★26 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.
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