by aashari
Integrate Jira with Atlassian Confluence to list, retrieve, and search spaces and pages, plus convert content to Markdow
Connects AI assistants to your Atlassian Confluence workspace so you can search, read, and manage pages and documentation using natural language.
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.
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.
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.
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
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
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
Share your MCP server with the developer community
We wired Atlassian Confluence into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, Atlassian Confluence benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Atlassian Confluence has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Strong directory entry: Atlassian Confluence surfaces stars and publisher context so we could sanity-check maintenance before adopting.
According to our notes, Atlassian Confluence benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Atlassian Confluence reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We wired Atlassian Confluence into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
I recommend Atlassian Confluence for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We evaluated Atlassian Confluence against two servers with overlapping tools; this profile had the clearer scope statement.
Atlassian Confluence is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
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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.
Get up and running in 2 minutes:
Generate a Confluence API Token:
# 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"}'
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.
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.
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".
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:
.env file in the current directory~/.mcp/configs.json (as shown above)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) |
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 objectSpaces:
/wiki/api/v2/spaces - List all spaces/wiki/api/v2/spaces/{id} - Get space detailsPages:
/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 labelsComments:
/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 commentBlog Posts:
/wiki/api/v2/blogposts - List blog posts/wiki/api/v2/blogposts/{id} - Get blog postSearch:
/wiki/rest/api/search - Search content (use cql query param)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:
When to use JSON instead:
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.
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:
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 conditionAsk your AI assistant:
Ask your AI assistant:
Ask your AI assistant:
Ask your AI assistant:
The CLI mirrors the MCP tools for direct terminal access. All commands support the same parameters as the tools.
get - GET any Confluence endpointpost - POST to any endpointput - PUT to any endpointpatch - PATCH any endpointdelete - DELETE from any endpointAll 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 (defPrerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ 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.