| name | openproject-mcp-connector |
| description | MCP connector for OpenProject – enables AI-driven project management, work package automation, Gantt chart analysis, and team coordination through Claude |
| license | GPL |
| tags | ["product","project-management","mcp","openproject","agile"] |
| difficulty | intermediate |
| time_to_master | 4-8 weeks |
| version | 1.0.0 |
Openproject MCP Connector
Overview
This skill enables Claude to interact with Openproject through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Openproject's REST API, allowing natural language control of Openproject operations, intelligent automation, and AI-powered assistance for Openproject workflows.
When to Use This Skill
- Work package creation and bulk updates via natural language
- Sprint planning and backlog prioritization with AI
- Gantt chart timeline analysis and conflict detection
- Meeting minutes to action items conversion
- Resource allocation optimization suggestions
Architecture
┌─────────────┐ ┌─────────────────┐ ┌──────────────────┐
│ Claude │────▶│ MCP Server │────▶│ Openproject │
│ (Client) │◀────│ (TypeScript) │◀────│ (REST API )│
└─────────────┘ └─────────────────┘ └──────────────────┘
Core Concepts
MCP Server Setup
The connector implements an MCP server that exposes Openproject operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.
Key Endpoints/Interfaces
/api/v3/work_packages, /api/v3/projects, /api/v3/time_entries, /api/v3/versions, /api/v3/queries
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: "openproject-mcp-connector",
version: "1.0.0",
});
server.tool(
"list_resources",
"List and query Openproject 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 }) => {
const response = await fetch(`${BASE_URL}/api/v3/work_packages`, {
headers: { "Authorization": `Bearer ${API_KEY}` },
});
const data = await response.json();
{
: [{ : , : .(data, , ) }],
};
}
);
server.(
,
,
{
: z.().(),
: z.({}).().().(),
},
({ name, config }) => {
response = (, {
: ,
: {
: ,
: ,
},
: .({ name, ...config }),
});
data = response.();
{
: [{ : , : }],
};
}
);
server.(
,
,
{
: z.().(),
: z.().().(),
},
({ , timeframe }) => {
response = (, {
: { : },
});
data = response.();
{
: [{ : , : .(data, , ) }],
};
}
);
transport = ();
server.(transport);
Claude Desktop Configuration
{
"mcpServers": {
"openproject-mcp-connector": {
"command": "node",
"args": ["path/to/openproject-mcp-connector/index.js"],
"env": {
"OPENPROJECT_API_KEY": "your-api-key",
"OPENPROJECT_BASE_URL": "https://your-instance-url"
}
}
}
}
Best Practices
- Authentication: Store API keys securely using environment variables; never hardcode credentials
- Rate Limiting: Implement request throttling to respect Openproject API rate limits
- Error Handling: Provide clear, actionable error messages for common failure scenarios
- Pagination: Handle paginated responses for large datasets efficiently
- Caching: Cache frequently accessed read-only data to reduce API calls
- Security: Validate all inputs before passing to the Openproject API; sanitize outputs
- Logging: Log all API interactions for debugging and audit purposes
Example Prompts
"Create a sprint backlog with 5 user stories from this requirements document and estimate story points"
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
Changelog
| Version | Date | Changes |
|---|
| 1.0.0 | 2026-04-01 | Initial MCP connector skill |
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