Brev
OfficialBrev MCP 서버
이는 Brev에 대한 MCP 서버 구현입니다.
구성
MCP 서버는 Brev CLI의 API 액세스 토큰과 현재 설정된 org.springframework.cloud.cloud_hosts를 사용합니다.
Brev 설명서 에 따라 CLI를 다운로드하고 아직 로그인하지 않았다면 로그인하세요.
Brev org를 전환하려면 brev set <org-name> 실행하세요.
CLI 액세스 토큰은 매시간 만료됩니다. 403 오류가 발생하면 brev ls 실행하여 액세스 토큰을 갱신하세요.
Related MCP server: GCP Infrastructure MCP Server
빠른 시작
로컬로 저장소 설정
git clone git@github.com:brevdev/brev-mcp.git
uv 설치
UV 설치 가이드를 따르세요
클로드 데스크탑
MacOS의 경우: ~/Library/Application\ Support/Claude/claude_desktop_config.json Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json
claude_desktop_config.json 에 다음을 추가하세요.
지엑스피1
개발
건축 및 출판
배포를 위해 패키지를 준비하려면:
종속성 동기화 및 잠금 파일 업데이트:
uv sync패키지 배포 빌드:
uv build이렇게 하면 dist/ 디렉토리에 소스와 휠 배포판이 생성됩니다.
PyPI에 게시:
uv publish참고: 환경 변수나 명령 플래그를 통해 PyPI 자격 증명을 설정해야 합니다.
토큰:
--token또는UV_PUBLISH_TOKEN또는 사용자 이름/비밀번호:
--username/UV_PUBLISH_USERNAME및--password/UV_PUBLISH_PASSWORD
디버깅
MCP 서버는 stdio를 통해 실행되므로 디버깅이 어려울 수 있습니다. 최상의 디버깅 환경을 위해서는 MCP Inspector 사용을 강력히 권장합니다.
다음 명령을 사용하여 npm 통해 MCP Inspector를 시작할 수 있습니다.
npx @modelcontextprotocol/inspector uv --directory /Users/tmontfort/Brev/repos/brev_mcp run brev-mcpInspector를 실행하면 브라우저에서 접근하여 디버깅을 시작할 수 있는 URL이 표시됩니다.
Available Tools
2 toolscreate_workspaceC
Create a workspace from an instance type and cloud provider
| Name | Required | Description | Default |
|---|---|---|---|
| cloud_provider | Yes | The cloud provider for the workspace | |
| instance_type | No | The instance type of the workspace | |
| name | No | The name of the workspace |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a creation tool, implying a write/mutation operation, but doesn't mention permission requirements, whether the operation is idempotent, what happens on failure, or any rate limits. This leaves significant behavioral gaps for a tool that creates resources.
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, efficient sentence that directly states the tool's purpose without any unnecessary words. It's appropriately sized and front-loaded with the essential information, making it easy to parse quickly.
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?
For a resource creation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what a 'workspace' represents in this context, what happens after creation, whether there are dependencies or constraints, or what the return value might be. The combination of mutation behavior and lack of structured metadata creates significant contextual 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?
The schema description coverage is 100%, with all parameters well-documented in the schema itself. The description mentions 'instance type and cloud provider' as inputs, which aligns with two of the three parameters, but doesn't add meaningful semantic context beyond what the schema already provides. The baseline of 3 is appropriate given the comprehensive schema documentation.
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 action ('Create') and resource ('workspace') with specific inputs ('from an instance type and cloud provider'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'get_instance_types', which appears to be a read-only counterpart rather than a direct alternative for creation.
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 or any prerequisites for its use. While it mentions 'instance type and cloud provider' as inputs, it doesn't clarify if this is the only way to create a workspace or if there are other methods available.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_instance_typesB
Get available instances types for a cloud provider
| Name | Required | Description | Default |
|---|---|---|---|
| cloud_provider | Yes | The cloud provider to get instance types for |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Get available instance types' but doesn't specify if this is a read-only operation, requires authentication, has rate limits, or what the output format might be. This leaves significant gaps in understanding the tool's behavior.
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 efficiently conveys the tool's purpose without any unnecessary words. It is front-loaded and appropriately sized, making it easy to parse quickly.
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 with full schema coverage) and lack of output schema, the description is minimally adequate. However, it doesn't compensate for the absence of annotations or output details, leaving the agent with incomplete context about the tool's full behavior and results.
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 100%, with the parameter 'cloud_provider' well-documented in the schema, including an enum list. The description adds no additional meaning beyond what the schema provides, such as explaining the significance of the provider choice, so it meets the baseline for high schema coverage.
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 action ('Get') and resource ('available instance types for a cloud provider'), making the purpose immediately understandable. However, it doesn't differentiate from the sibling tool 'create_workspace', which is unrelated, so it doesn't fully earn the highest score for sibling distinction.
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 or any prerequisites. It simply states what it does without context about timing, constraints, or comparisons to other tools, leaving the agent with minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v1.0.0- First observed
create_workspace - First observed
get_instance_types
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: one creates a workspace and the other retrieves instance types. There is no overlap in functionality, making it impossible for an agent to confuse them.
Both tools follow a consistent verb_noun naming pattern (create_workspace, get_instance_types). The verbs are clear and descriptive, and the style is uniform throughout.
With only two tools, the server feels thin for managing workspaces. It lacks essential operations like listing, updating, or deleting workspaces, which limits its utility for typical lifecycle management.
The toolset is severely incomplete for workspace management. It includes creation and instance type lookup but omits critical operations such as listing existing workspaces, updating configurations, or deleting workspaces, leaving significant gaps in coverage.
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