Provides read-only access to SonarQube projects, metrics, and rules, allowing users to search for issues, retrieve quality dashboards, and access detailed information about code bugs, vulnerabilities, and security hotspots.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@SonarQube MCP Server list all critical bugs and vulnerabilities in the 'web-app' project"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A read-only Model Context Protocol (MCP) server that gives AI assistants structured access to SonarQube — issues, metrics, rules, and projects.
6 read-only tools covering the full SonarQube quality workflow
Safe by design — no mutations, every tool returns a consistent {"ok": ...} envelope
Input validation before any API call (severities, types, statuses)
Structured errors with machine-readable error_code fields
Works with SonarQube Community, Developer, and Enterprise editions
Related MCP server: sonarqube-api-mcp
Python 3.10+
A running SonarQube instance
A SonarQube user token (squ_...)
pip install sonarqube-mcp-serverOr install from source:
git clone <repo>
cd sonarqube-mcp
pip install -e .SONARQUBE_URL=http://localhost:9000 \
SONARQUBE_TOKEN=squ_xxxxxxxxxxxx \
sonarqube-mcp-serverOr with python -m:
python -m sonarqube_mcpVariable | Required | Default | Description |
| No |
| Base URL of your SonarQube instance |
| Yes | — | User token for authentication |
| No |
| HTTP request timeout in seconds |
Copy .env.example to .env and fill in your values:
cp .env.example .envIn SonarQube: My Account → Security → Generate Tokens. A user token (squ_...) with Browse permission on the target projects is sufficient for all read-only operations.
Add to your Claude Code MCP settings (~/.claude/claude_code_config.json):
{
"mcpServers": {
"sonarqube": {
"command": "sonarqube-mcp-server",
"env": {
"SONARQUBE_URL": "http://localhost:9000",
"SONARQUBE_TOKEN": "squ_xxxxxxxxxxxx"
}
}
}
}Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"sonarqube": {
"command": "sonarqube-mcp-server",
"env": {
"SONARQUBE_URL": "http://localhost:9000",
"SONARQUBE_TOKEN": "squ_xxxxxxxxxxxx"
}
}
}
}All tools are read-only (readOnlyHint: true, destructiveHint: false).
check_statusVerify connectivity and retrieve server version.
{}Response:
{
"ok": true,
"server_url": "http://localhost:9000",
"status": "UP",
"version": "10.4.1"
}list_projectsList or search SonarQube projects with pagination.
Parameter | Type | Default | Description |
|
| — | Filter by project name or key |
|
|
| Page number (1-indexed) |
|
|
| Results per page (1–500) |
Response:
{
"ok": true,
"total": 42,
"page": 1,
"page_size": 20,
"projects": [
{"key": "my-app", "name": "My Application", "qualifier": "TRK"}
]
}search_issuesSearch issues across all projects or scoped to one project, with rich filtering.
Parameter | Type | Default | Description |
|
| — | Scope to a specific project |
|
| — | CSV: |
|
| — | CSV: |
|
| — | CSV: |
|
| — | CSV of tag names |
|
| — |
|
|
|
| Page number |
|
|
| Results per page (1–500) |
Example — find all open blockers in a project:
{
"project_key": "my-app",
"severities": "BLOCKER,CRITICAL",
"statuses": "OPEN"
}get_issueGet full detail for a single issue by key, including text range, effort, assignee, comments, and data-flow information.
Parameter | Type | Description |
|
| Issue key (e.g. |
get_project_metricsRetrieve the quality dashboard for a project. Returns a standard set of metrics by default, or a custom selection.
Parameter | Type | Default | Description |
|
| — | Required. Project key |
|
| See below | CSV of metric keys |
Default metrics: bugs, vulnerabilities, code_smells, security_hotspots, coverage, duplicated_lines_density, ncloc, sqale_index, reliability_rating, security_rating, sqale_rating, alert_status, quality_gate_details
Response:
{
"ok": true,
"project_key": "my-app",
"project_name": "My Application",
"metrics": {
"bugs": "3",
"coverage": "78.4",
"alert_status": "OK"
}
}get_ruleRetrieve the description and metadata for a SonarQube rule.
Parameter | Type | Description |
|
| Rule key (e.g. |
All tools return a consistent envelope. On failure:
{
"ok": false,
"error_code": "auth_error",
"message": "Authentication failed. Check SONARQUBE_TOKEN.",
"details": {}
}
| Cause |
| Invalid or missing token (HTTP 401) |
| Token lacks permissions (HTTP 403) |
| Project, issue, or rule does not exist (HTTP 404) |
| Cannot reach the SonarQube instance |
| Request exceeded |
| Bad parameter value (e.g. unknown severity) |
| Other non-2xx SonarQube response |
| Unexpected server-side error |
pip install -e ".[dev]"pytestsrc/sonarqube_mcp/
├── server.py # FastMCP server, tool definitions, main()
├── sonarqube_client.py # httpx.Client wrapper for SonarQube REST API
├── settings.py # Frozen dataclass + env var loading
├── errors.py # SonarQubeError + error_response()
├── __main__.py # python -m sonarqube_mcp entrypoint
└── __init__.py
tests/
├── conftest.py
├── test_server.py
├── test_client.py
├── test_settings.py
└── test_errors.pycreate_server() is a factory that captures settings and client in closure scope — makes unit testing straightforward by injecting a pre-built SonarQubeSettings.
@_safe_tool wraps every tool so it never raises — exceptions are caught and returned as structured error envelopes.
_clamp() keeps page_size within SonarQube's supported API limits (1–500).
Settings use a frozen=True dataclass — immutable after load, safe to share across tool closures.
MIT
Verify SonarQube connectivity and return server version/status.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context about what the tool returns (server version/status), which is useful beyond the annotations. Annotations already cover read-only, non-destructive, idempotent, and closed-world hints, so the bar is lower. The description doesn't contradict annotations and provides some behavioral insight, but lacks details like error handling or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that front-loads the key action ('verify connectivity') and outcome ('return server version/status'). There is no wasted verbiage, and it efficiently conveys the essential information without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, annotations provide safety hints, and an output schema exists), the description is sufficiently complete. It explains what the tool does and what it returns, and with the output schema handling return values, no additional detail is needed. However, it could slightly improve by mentioning it's a health check tool relative to siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters and 100% schema description coverage, the schema fully documents the lack of inputs. The description doesn't need to add parameter details, and it appropriately focuses on the tool's purpose without redundancy. A baseline of 4 is applied since no parameters are present, and the description efficiently avoids unnecessary information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('verify connectivity' and 'return server version/status') and identifies the resource (SonarQube). It distinguishes from siblings by focusing on connectivity/status rather than issues, projects, or rules. However, it doesn't explicitly contrast with sibling tools in the description text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for connectivity verification and status checking, which suggests when to use it (e.g., for health checks). However, it doesn't provide explicit guidance on when to use this versus alternatives like checking project metrics or issues, nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information for a single SonarQube issue by key.
Args: issue_key: The issue key (e.g., AXy1k...).
| Name | Required | Description | Default |
|---|---|---|---|
| issue_key | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate this is a safe, read-only, idempotent operation with a closed-world assumption. The description adds minimal behavioral context beyond this, such as specifying it retrieves 'detailed information' for a single issue, but does not elaborate on response format, error handling, or other traits like rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by a brief parameter explanation. It is appropriately sized with zero wasted words, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter), rich annotations covering safety and behavior, and the presence of an output schema, the description is reasonably complete. It could be improved by clarifying distinctions from sibling tools, but it adequately supports tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, but the description provides the parameter 'issue_key' with an example ('AXy1k...'), adding meaning beyond the bare schema. However, it does not fully compensate for the lack of schema descriptions, such as explaining key format constraints or validation rules.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get detailed information for a single SonarQube issue by key.' It specifies the verb ('Get'), resource ('SonarQube issue'), and scope ('single'), but does not explicitly differentiate it from sibling tools like 'search_issues' or 'get_rule', which prevents a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools such as 'search_issues' for multiple issues or 'get_rule' for rule details, nor does it specify prerequisites or exclusions, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get quality metrics for a SonarQube project.
Args: project_key: The project key. metric_keys: Comma-separated metric keys. Defaults to a standard quality dashboard set (bugs, coverage, smells, ratings, etc.).
| Name | Required | Description | Default |
|---|---|---|---|
| project_key | Yes | ||
| metric_keys | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide key behavioral hints: read-only, non-destructive, idempotent, and closed-world. The description adds minimal context by specifying it's for SonarQube projects, but it doesn't disclose additional traits like rate limits, authentication needs, or what the metrics entail. It doesn't contradict annotations, but adds little value beyond them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first. The 'Args' section is structured but could be more integrated. It avoids unnecessary details, though the formatting as a code block might be slightly verbose for pure conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, and the presence of an output schema, the description is reasonably complete. It covers the basic purpose and parameters, and the output schema handles return values. However, it lacks usage guidelines and deeper parameter explanations, which slightly limits completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter details. The description compensates by explaining 'project_key' as 'The project key' and 'metric_keys' as 'Comma-separated metric keys' with a default set, adding basic semantics. However, it doesn't fully clarify formats or examples, leaving gaps for a tool with 2 parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get quality metrics for a SonarQube project.' It specifies the verb ('Get') and resource ('quality metrics for a SonarQube project'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_projects' or 'check_status', which might also relate to project information, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_projects' or 'search_issues', nor does it specify prerequisites or exclusions. The only implied context is for retrieving metrics, but this is too vague for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the description and metadata for a SonarQube rule.
Args: rule_key: Rule key (e.g., python:S1192, java:S106).
| Name | Required | Description | Default |
|---|---|---|---|
| rule_key | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover key behavioral traits: read-only, non-destructive, idempotent, and closed-world. The description adds context by specifying it retrieves 'description and metadata,' which clarifies the scope of data returned. However, it does not disclose additional behaviors like rate limits, authentication needs, or error handling, leaving some gaps despite annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with a clear purpose statement followed by parameter details in a separate 'Args' section. Every sentence adds value, and there is no unnecessary information. It could be slightly improved by integrating the parameter explanation more seamlessly, but overall it is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter), rich annotations, and presence of an output schema, the description is reasonably complete. It explains what the tool does and provides parameter semantics, which compensates for the low schema coverage. However, it lacks usage guidelines and could better integrate with sibling tools, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides a clear example for the 'rule_key' parameter ('e.g., python:S1192, java:S106'), adding meaningful semantics beyond the schema's basic type definition. This effectively explains the parameter's format and usage, though it could benefit from more detail on valid rule keys.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the description and metadata for a SonarQube rule.' It specifies the verb ('Get') and resource ('description and metadata for a SonarQube rule'), making it easy to understand what the tool does. However, it does not explicitly differentiate from sibling tools like 'get_issue' or 'get_project_metrics', which might also retrieve metadata, so it misses full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'get_issue' or 'search_issues', nor does it specify contexts or exclusions for usage. The only implied usage is retrieving rule details, but this is insufficient for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List or search SonarQube projects with pagination.
Args: query: Optional search string to filter project names/keys. page: Page number (1-indexed). page_size: Results per page (1–500).
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | ||
| page | No | ||
| page_size | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds pagination behavior and search capability context, which is useful but doesn't disclose rate limits, authentication needs, or return format details beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement followed by parameter explanations. Every sentence adds value, and it's appropriately sized without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations covering safety and behavior, and the presence of an output schema (which handles return values), the description is complete enough. It explains the core functionality and parameters adequately for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all three parameters: query (search filter), page (1-indexed page number), and page_size (results per page with range). It adds meaningful semantics beyond the bare schema, though it doesn't specify default values already in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List or search') and resource ('SonarQube projects'), and distinguishes it from siblings by specifying it's for projects rather than issues, rules, or metrics. The mention of pagination further clarifies scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (listing/searching projects with pagination) but doesn't explicitly state when to use this tool versus alternatives like get_project_metrics or search_issues. It provides clear operational context but lacks explicit sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search SonarQube issues with filters.
Args: project_key: Project key to scope issues. severities: Comma-separated: INFO,MINOR,MAJOR,CRITICAL,BLOCKER. types: Comma-separated: CODE_SMELL,BUG,VULNERABILITY,SECURITY_HOTSPOT. statuses: Comma-separated: OPEN,CONFIRMED,REOPENED,RESOLVED,CLOSED. tags: Comma-separated tag names. assigned: Filter by assignment (true=assigned, false=unassigned). page: Page number (1-indexed). page_size: Results per page (1–500).
| Name | Required | Description | Default |
|---|---|---|---|
| project_key | No | ||
| severities | No | ||
| types | No | ||
| statuses | No | ||
| tags | No | ||
| assigned | No | ||
| page | No | ||
| page_size | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only, non-destructive, idempotent, and closed-world. The description adds useful context about pagination behavior (1-indexed page, 1-500 page size) and filter syntax (comma-separated values), but doesn't mention rate limits, authentication needs, or what happens when filters return no results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a brief purpose statement followed by parameter documentation. Every sentence adds value, though the formatting as a bullet list could be more front-loaded with key usage information before parameter details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has annotations covering safety profile and an output schema exists (so return values needn't be explained), the description provides adequate context. It fully documents all parameters and their semantics, though could benefit from more behavioral context about error cases or performance characteristics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description carries full burden for explaining parameters. It successfully documents all 8 parameters with clear semantics: project scoping, filter options (severities, types, statuses, tags, assignment), and pagination details including valid ranges and defaults. This fully compensates for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches SonarQube issues with filters, providing a specific verb ('search') and resource ('SonarQube issues'). However, it doesn't explicitly differentiate from sibling tools like 'get_issue' or 'list_projects', which could also retrieve issue-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_issue' (for single issues) or 'list_projects' (for project overview). It mentions filtering capabilities but doesn't specify use cases or prerequisites for effective searching.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose with no overlap: connectivity check, single issue retrieval, project metrics, rule details, project listing, and issue searching. The descriptions specify different resources (server, issue, project, rule) and actions (check, get, list, search), making misselection unlikely.
All tools follow a consistent verb_noun pattern (e.g., check_status, get_issue, list_projects, search_issues). The verbs are appropriate and predictable, with no deviations in style or convention across the set.
Six tools is well-scoped for a SonarQube server, covering core functionalities like status verification, issue and project management, metrics, and rule lookup. Each tool earns its place without feeling excessive or insufficient for the domain.
The toolset provides strong coverage for querying and monitoring in SonarQube, including status, projects, issues, metrics, and rules. A minor gap exists in write operations (e.g., creating or resolving issues), but agents can still perform most read-oriented tasks effectively.
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