Fetch GitHub issue details using MCP server for efficient task management
The MCP GitHub Issue Server (MCP GISS) is a specialized MCP server that allows artificial intelligence applications, such as Claude Desktop and Continue, to leverage GitHub issues as tasks. This unique integration enables LLMs (Large Language Models) to fetch and process issue details directly from public repositories, treating them as detailed task descriptions within the Model Context Protocol framework.
MCP GISS is designed with several key features that enhance its utility for AI applications:
graph TD
A[AI Application] -->|MCP Client| B[MCP Protocol]
B --> C[MCP Server - GitHub Issue]
C --> D[GitHub API]
D --> E[Database/GitHub Repository]
style A fill:#e1f5fe
style C fill:#f3e5f5
style D fill:#e8f5e8
graph TD
subgraph "API Layer"
API
end
subgraph "Data Source Layer"
DB[Database/GitHub Repository]
end
M[Servers - MCP Protocol Stack]
D[AI Application]
subgraph "MCP Client"
CLT[Claude Desktop, Continue, Cursor]
end
M --> D --> API
API --> C[MCP Server - GitHub Issue]
C --> DB
The architecture of MCP GISS is designed to seamlessly integrate with the Model Context Protocol (MCP), ensuring a standardized and consistent way for AI applications to interact with various data sources. The server leverages the npx command-line utility to manage its operations, making it straightforward to deploy and run.
graph TD
A[AI Application] -->|MCP Client| B[MCP Protocol]
B --> C[MCP Server - GitHub Issue]
C --> D[GitHub API]
D --> E[Database/GitHub Repository]
style A fill:#e1f5fe
style C fill:#f3e5f5
style D fill:#e8f5e8
graph TB
subgraph "API Layer"
API[GitHub API]
end
subgraph "Data Source Layer"
DB[Database/GitHub Repository]
end
M[Servers - MCP Protocol Stack]
D[AI Application]
subgraph "MCP Client"
CLT[Claude Desktop, Continue, Cursor]
end
M --> D --> API
API --> C[MCP Server - GitHub Issue]
C --> DB
To get MCP GISS up and running for use in your AI applications, follow these installation steps:
npx mcp-github-issue
For automatic installation through [Smithery], run the following command to install the MCP GitHub Issue Server specifically for Claude Desktop.
npx -y @smithery/cli install mcp-github-issue --client claude
Integrate MCP GISS with CLAude Desktop to enable seamless tracking and resolution of bugs from GitHub repositories. When a new issue is raised, the server fetches details and presents them as structured tasks for the model to process.
Example Implementation:
<use_mcp_tool>
<server_name>github-issue</server_name>
<tool_name>get_issue_task</tool_name>
<arguments>
{
"url": "https://github.com/owner/repo/issues/123"
}
</arguments>
Use MCP GISS to generate documentation based on specific GitHub issues. This can be particularly useful for maintaining accurate and up-to-date documentation related to project-specific tasks or bugs.
Technical Implementation:
<use_mcp_tool>
<server_name>github-issue</server_name>
<tool_name>get_issue_task</tool_name>
<arguments>
{
"url": "https://github.com/owner/repo/issues/123"
}
</arguments>
MCP GISS is fully compatible and seamlessly integrated with various MCP clients, including:
| MCP Client | Resources |
|---|---|
| Claude Desktop | ✅ |
| Continue | ✅ |
| Cursor | ❌ |
This ensures that your AI application can leverage the server's capabilities without any compatibility issues.
The MCPS GISS is designed to perform well within the Model Context Protocol framework and has been tested with several key clients:
| MCP Client | Resources | Tools | Prompts |
|---|---|---|---|
| Claude Desktop | ✅ | ✅ | ✅ |
| Continue | ✅ | ✅ | X |
| Cursor | ❌ | ✅ | ❌ |
Add the following configuration to your MCP setup to include the GitHub Issue Server:
{
"mcpServers": {
"github-issue": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github-issue"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
Ensure that sensitive information, such as API keys, is stored securely. MCP GISS supports environment variables like API_KEY to manage sensitive data.
{
"env": {
"API_KEY": "your-api-key"
}
}
To keep the code clean and consistent, run the formatting tool periodically:
npm run format
How does MCP GISS ensure data privacy?
The server strictly follows privacy guidelines for public repository interactions but requires users to handle sensitive information securely.
Can I customize specific queries or filters when fetching issues?
Yes, you can extend the server's capabilities with custom query options and filters to tailor issue fetching according to your needs.
Are there any performance considerations while using MCP GISS in large projects?
Performance is optimized for both small and large-scale projects. However, depending on the volume of issues, caching mechanisms may be necessary to maintain responsiveness.
How does the server handle authentication for private repositories?
Private repositories require authentication, which can be managed through environment variables or configuration settings.
Is there any ongoing maintenance required for MCP GISS?
Regular updates and monitoring are recommended but minimal efforts are needed as the protocol handles most of the infrastructure.
If you wish to contribute to the development of MCP GISS, follow these guidelines:
.eslintrc and run linting before committing.For more information on the Model Context Protocol and other MCP servers, visit:
By integrating MCP GISS into your AI workflows, you can enhance the utility of LLMs by providing them with rich contextual data directly from GitHub issues. This integration supports a wide range of use cases and ensures that your AI solutions are always up-to-date and relevant.
MCP GitHub Issue Server is an essential tool for developers looking to integrate GitHub issues into their AI applications seamlessly through the Model Context Protocol. By leveraging this server, you can enhance the efficiency and effectiveness of your AI-driven workflows while ensuring compatibility across various MCP clients.
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