web-browser-mcp-server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined as extracting webpage content with optional element selection.
Naming Consistency5/5The single tool name follows a clear verb_noun pattern (browse_webpage). Since there are no other tools to compare against, consistency is inherently perfect.
Tool Count2/5A single tool for a web browser server is too minimal for the apparent scope. Basic web interactions like navigation, clicking, form filling, or handling multiple tabs are missing, making the server feel incomplete and limiting for agents.
Completeness2/5The server is severely incomplete for a web browser domain. It only offers content extraction, lacking essential operations such as navigation, interaction with page elements, or session management, which are critical for typical web automation tasks.
Average 2.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No issues 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.
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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
- Behavior2/5
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 states the tool extracts content but fails to describe critical behaviors such as error handling (e.g., invalid URLs, network issues), performance traits (e.g., timeouts, rate limits), or output format. This leaves significant gaps in understanding how the tool operates beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more informative without sacrificing brevity. The structure is clear and direct, earning a high score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (web scraping with optional selectors), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'content' includes (e.g., text, links, structure), how selectors are applied, or potential limitations (e.g., JavaScript-rendered content). This leaves the agent with insufficient context for reliable use.
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?
Schema description coverage is 100%, so the schema already documents both parameters ('url' and 'selectors') adequately. The description adds minimal value by mentioning 'optional CSS selectors for specific elements,' which aligns with the schema but doesn't provide additional syntax, examples, or constraints. This 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.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Extract') and resource ('content from a webpage'), and mentions optional CSS selectors for refinement. It distinguishes the tool's core function effectively, though without sibling tools, differentiation isn't applicable. However, it lacks specificity about what 'content' entails (e.g., text, HTML, metadata), which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 versus alternatives, prerequisites, or limitations. It mentions optional CSS selectors but doesn't explain when they are beneficial or necessary. With no sibling tools, context for usage is minimal, but the absence of any usage context results in a low score.
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.
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