Browser-use MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'execute_browser_task' has a clearly distinct purpose that cannot be confused with any other tool in the set.
Naming Consistency5/5The naming is perfectly consistent as there is only one tool, following a clear verb_noun pattern ('execute_browser_task'). No other naming conventions are present to cause inconsistency.
Tool Count2/5A single tool for a browser automation server is too few for the apparent scope, as browser automation typically involves multiple operations like navigation, clicking, typing, or scraping. This feels thin and may limit functionality.
Completeness1/5The tool surface is severely incomplete for browser automation, lacking essential operations such as navigating to URLs, interacting with elements, or extracting data. This will likely cause agent failures in handling common browser tasks.
Average 2/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 2 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description only states what the tool does at a high level ('execute browser automation tasks') without revealing any behavioral traits such as whether it's read-only or destructive, authentication requirements, rate limits, error handling, or what happens during execution. This leaves the agent with insufficient information to understand 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single phrase in Chinese that directly states the tool's function. There's no wasted language or unnecessary elaboration, making it front-loaded and efficient. However, this conciseness comes at the cost of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there are no annotations and no output schema, the description is incomplete for a tool that performs browser automation. It fails to explain what the tool returns, what side effects it might have, error conditions, or any behavioral context. For a potentially complex automation tool with zero structured metadata, the description is grossly inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage with one parameter 'task' documented as '要执行的任务描述' (description of the task to execute). The description adds no additional meaning beyond what the schema provides—it doesn't explain what constitutes a valid task description, provide examples, or clarify semantics. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5Does the description clearly state what the tool does and how it differs from similar tools?
The description '执行浏览器自动化任务' (Execute browser automation tasks) is a tautology that essentially restates the tool name 'execute_browser_task' in Chinese. It provides a generic verb+resource combination but lacks specificity about what types of browser automation tasks are supported or what resources are affected. No sibling tools exist for differentiation, but the description remains vague about the actual purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does 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, such as what scenarios it's designed for, prerequisites, or limitations. There are no alternatives mentioned (though none exist as siblings), but the description fails to give any context about appropriate usage scenarios or constraints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/win4r/browser-use-MCP-Server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server