iflow-mcp-sertaykabuk-sonarqube-mcp 0.1.1

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SonarQube MCP Server

This project packages a Model Context Protocol (MCP) server for AI coding agents that need to investigate and remediate SonarQube issues. The server is built with FastMCP and wraps the official SonarQube REST API to deliver:

• High-signal issue search results that highlight severity, ownership, and file locations.
• Detailed remediation context for a specific SonarQube issue, including the associated rule description and fix suggestions.
• Direct access to rule metadata for bespoke workflows.

🧠 Goal: Provide enough structured context for an agent to understand an issue, locate the offending code, and apply the recommended fix without visiting the SonarQube UI.

Requirements

• Python 3.12+
• Access to a SonarQube instance (cloud or self-hosted) with an API token that can read issues and rules.
• The fastmcp package is already included; no MCP client is bundled.

Configuration

You must configure the base URL of your SonarQube instance. Authentication can be provided in two ways:

1. Per-user Bearer token via an Authorization: Bearer <token> HTTP header when using the HTTP/SSE transports (recommended – allows each user/agent to have least-privilege access).
2. A static environment variable token (SONARQUBE_TOKEN) used as a fallback when no header is present (needed for stdio transport where HTTP headers are not available).

Set the following environment variables before starting the server:

Variable	Description	Example
SONARQUBE_BASE_URL	Base URL of your SonarQube instance (no trailing slash)	https://sonarqube.internal
SONARQUBE_TOKEN (optional)	Fallback personal access token with Browse access to projects. Used only if no Authorization header is supplied.	squ_XXXXXXXXXXXXXXXXXXXXXXXXXXXX
SONARQUBE_TIMEOUT (optional)	Request timeout in seconds (defaults to 15)	20

You can store these in a .env file or provide them through your process manager.

Installation

uv sync

For development and testing dependencies:

uv sync --extra test

Running the server

By default the server runs over stdio, which is ideal for local MCP clients:

uv run python -m src.main

To expose an HTTP transport, add the appropriate options when you run it:

uv run python -m src.main --transport http --host 0.0.0.0 --port 8765

Supported transports mirror the ones from FastMCP (stdio, http, sse). When using http/sse, send an Authorization header with each request so the server can perform SonarQube calls on behalf of that user:

Authorization: Bearer squ_XXXXXXXXXXXXXXXXXXXXXXXXXXXX

If the header is omitted the server will fall back to SONARQUBE_TOKEN.

Header-based auth example

The server internally inspects incoming headers (via FastMCP's get_http_headers) on every tool call. A simplified example:

from fastmcp import FastMCP
from fastmcp.server.dependencies import get_http_headers

mcp = FastMCP(name="Demo")

@mcp.tool
async def who_am_i() -> dict:
	headers = get_http_headers()
	auth = headers.get("authorization", "") if headers else ""
	return {
		"has_auth": bool(auth),
		"auth_is_bearer": auth.lower().startswith("bearer "),
		"user_agent": headers.get("user-agent") if headers else None,
	}

This mirrors the mechanism used by the SonarQube tools to resolve the active token for each request.

VS Code MCP client configuration

If you are using the VS Code built‑in MCP client (or an extension that reads a mcp.json style manifest), you can prompt the user for their SonarQube token and pass it as a bearer header. Example configuration snippet:

{
	"servers": {
		"sonarqube": {
			"url": "http://localhost:8765/mcp",
			"type": "http",
			"headers": {
				"Authorization": "Bearer ${input:sonarqube_token}"
			}
		}
	},
	"inputs": [
		{
			"id": "sonarqube_token",
			"type": "promptString",
			"description": "SonarQube Token"
		}
	]
}

Notes:

• The ${input:...} placeholder ensures the token is not stored in plain text in the config file; the user will be prompted in VS Code.
• If you prefer not to prompt each time, you can instead set the environment variable SONARQUBE_TOKEN and omit the headers block (less granular for multi-user scenarios).
• When using a remote SonarQube instance over HTTPS behind a corporate CA, ensure your Python environment trusts that CA (e.g. via REQUESTS_CA_BUNDLE / SSL_CERT_FILE).

Available tools

Tool	Purpose	Key arguments
get_issue_context	Fetch a single issue by key, pull its rule, and return a markdown-rich brief plus machine-readable metadata.	issue_key (string)
search_issues	Wrapper around /api/issues/search that exposes the most common filters and augments each result with the component path.	issue_keys, components, severities, issue_statuses, resolutions, types, tags, assignees, languages, created_after, created_before, resolved, sort_field, ascending, page, page_size
get_rule	Retrieve raw rule metadata from /api/rules/show.	rule_key (string)

All tools return JSON-serialisable dictionaries, making them easy to chain in agent workflows.

Example workflow

1. Call search_issues with project="Agrega-Server" to retrieve open issues.
2. For each issue, call get_issue_context(issue_key).
3. Use the markdown field to brief an LLM, and the structured issue/rule sections to build automated fixes.

Sample API responses from the SonarQube reference instance are provided in sample_search_api_response.json and sample_rule_api_response.json.

Testing

After installing the optional test extra:

uv run python -m pytest

The tests stub external HTTP calls using respx, so they run offline and quickly.

Troubleshooting

• If the server starts but every tool call fails immediately, ensure the Authorization header is accepted by your SonarQube instance and that your token has permission to view the project/issue.
• You can invoke the configuration_status tool (only available when the server is misconfigured) to diagnose missing environment variables.
• Enable HTTP-level logging by setting HTTPX_LOG_LEVEL=debug when running the process for deeper visibility.

Roadmap / Ideas

• Surface code snippets when available via the flows metadata.
• Cache rule metadata to reduce duplicate requests during long sessions.
• Add tooling metrics (latency, error rates) to help identify flaky integrations.