在 AGI House MCP Hackathon 建造
该项目是一个浏览器自动化代理，它使用模型上下文协议 (MCP) 来实现浏览器交互。它通过我们的 MCP 服务器实现了 Claude 与浏览器自动化功能的无缝集成。
感谢 Browser-Use 的浏览器代理功能，为我们的 MCP 服务器提供支持！
Related MCP server: selenium-mcp
macOS（达尔文 24.2.0）
Python 3.12 或更高版本
uv包管理器
Google Chrome 浏览器（运行任务之前请确保浏览器已关闭。）
要通过Smithery自动安装 Claude Desktop 的浏览器自动化代理：
npx -y @smithery/cli install @ashley-ha/mcp-manus --client claude克隆存储库：
git clone <repository-url>
cd mcp使用uv设置 Python 环境：
uv venv
source .venv/bin/activate
uv sync创建或修改您的 Claude Desktop 配置文件：
{
"mcpServers": {
"browser-use": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/mcp",
"run",
"browser-use.py"
]
}
}
}将/ABSOLUTE/PATH/TO/browser-use替换为项目目录的绝对路径。
该代理配置为使用 Google Chrome，具有以下默认设置：
用于开发的非无头模式
窗口大小：1280x1100
禁用安全功能以进行测试
录音路径：./tmp/recordings
通过 MCP 工具实现浏览器自动化
国家管理和规划能力
交互元素检测和操作
可配置的浏览器上下文
日志记录和调试支持
该代理提供两个主要工具：
get_planner_state ：检索当前浏览器状态和规划上下文
execute_actions浏览器中计划的操作
该项目使用 Python 的内置日志记录，其配置如下：
所有日志都指向 stderr
自定义格式： %(levelname)-8s [%(name)s] %(message)s
根记录器级别：INFO
第三方记录器级别：警告
browser-use.py ：主入口点和服务器实现
tmp/recordings ：浏览器会话记录的目录
通过uv管理依赖项
该项目是在 AGI House MCP Hackathon 期间构建的。欢迎贡献！
该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅LICENSE文件。
版权所有 (c) 2025 Jaeyun Ha、Ashley Ha
特此授予获得此软件和相关文档文件（“软件”）副本的任何人免费许可，以无限制方式处理软件，包括但不限于使用、复制、修改、合并、发布、分发、再授权和/或销售软件副本的权利，并允许向其提供软件的人员这样做，但须遵守以下条件：
上述版权声明和本许可声明均应包含在软件的所有副本或实质性部分中。
本软件按“原样”提供，不附带任何形式的明示或暗示保证，包括但不限于适销性、适用于特定用途和非侵权性的保证。在任何情况下，作者或版权所有者均不对因本软件或使用或以其他方式处理本软件而引起的或与之相关的任何索赔、损害或其他责任承担责任，无论是合同、侵权或其他诉讼。
Execute actions from the planner state.
Args:
actions: A dictionary containing the planner state and actions in format:
{
"current_state": {
"evaluation_previous_goal": str,
"memory": str,
"next_goal": str
},
"action": [
{"action_name": {"param1": "value1"}},
...
]
}
Note: If the page state changes (new elements appear) during action execution,
the sequence will be interrupted and you'll need to get a new planner state.
| Name | Required | Description | Default |
|---|---|---|---|
| actions | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that execution can be interrupted by page state changes, which is a key behavioral trait, but doesn't cover other aspects like error handling, side effects, or response format. It adds some context but is incomplete for a mutation tool.
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 main purpose, followed by an 'Args' section and a note. The structure is clear, but the note could be more integrated; overall, it's efficient with minimal waste.
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 complexity (1 parameter with nested objects, no annotations, no output schema), the description covers the parameter structure well and includes a behavioral note. However, it lacks details on return values, error cases, and full usage context, making it adequate but with gaps for a tool that likely performs mutations.
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%, so the description must compensate. It provides a detailed example of the 'actions' parameter structure, including nested objects and keys like 'current_state' and 'action', which adds significant meaning beyond the schema's generic 'object' type. This effectively documents the parameter semantics.
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 states the tool 'Execute actions from the planner state', which provides a verb ('Execute') and resource ('actions from the planner state'), but it's vague about what 'actions' specifically entail (e.g., UI interactions, API calls) and doesn't clearly distinguish from the sibling tool 'get_planner_state'. It's not tautological but lacks specificity.
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 includes a note about interruption when 'the page state changes', which implies a usage context (e.g., web automation), but it doesn't explicitly state when to use this tool versus alternatives like 'get_planner_state' or provide prerequisites. The guidance is minimal and not comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the current browser state and planning context. This tool must be executed before execute_actions tool.
Must return a JSON string in the format:
{
"current_state": {
"evaluation_previous_goal": "Success|Failed|Unknown - Analysis of previous actions",
"memory": "Description of what has been done and what to remember",
"next_goal": "What needs to be done with the next immediate action"
},
"action": [
{"action_name": {"param1": "value1", ...}},
...
]
}
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it's a read operation ('Get'), it returns specific structured data (a JSON string with defined format), and it has a prerequisite relationship with another tool. It doesn't cover aspects like error handling or performance, but for a zero-parameter tool with no annotations, this is reasonably comprehensive.
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 and usage guideline in the first two sentences, which is good. However, it includes a detailed JSON format specification that might be better suited for an output schema. While this adds value, it makes the description longer than necessary for conciseness, as the output details could be separated into structured data.
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 0 parameters, no annotations, and no output schema, the description provides good contextual completeness. It explains the purpose, usage guidelines, and output format in detail. The only gap is the lack of an output schema, but the description compensates by specifying the return format explicitly, making it sufficient for the agent to understand how to use the tool.
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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to add parameter information, and it doesn't attempt to, which is appropriate. No parameters are present to document, so this score reflects that the description doesn't introduce confusion or redundancy regarding inputs.
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 current browser state and planning context.' It specifies the verb ('Get') and resource ('browser state and planning context'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling tool 'execute_actions' beyond stating a prerequisite relationship, which is more about usage than purpose 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 explicit usage guidance: 'This tool must be executed before execute_actions tool.' It clearly states when to use this tool (as a prerequisite for 'execute_actions') and implies an alternative (use 'execute_actions' after this). This is a strong, directive guideline that helps the agent understand the tool's role in the workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
execute_actionsget_planner_stateScored across 2 tools
The two tools have completely distinct purposes: get_planner_state retrieves browser state and planning context, while execute_actions performs actions based on that state. There is no overlap or ambiguity between these functions.
Both tools follow a consistent verb_noun pattern with clear action-oriented names (get_planner_state, execute_actions). The naming convention is uniform and predictable throughout the set.
With only 2 tools for a browser automation server, the surface feels severely limited. While the tools cover a basic planning-execution loop, typical browser automation requires more granular operations like navigation, element interaction, or content extraction.
The toolset provides only a high-level planning/execution abstraction without direct browser manipulation capabilities. There are significant gaps for common browser tasks like navigating to URLs, clicking elements, extracting text, or handling dialogs, which agents would need for robust automation.
AI-powered web automation. Navigate websites using AI agents for one page or a thousand
AI-powered web automation. Navigate websites using AI agents for one page or a thousand
Autonomous cloud agent tasks: real browser + your tools, structured evidence-backed results.
AI-powered browser automation — navigate, click, fill forms, and extract data from any website.
