WebEvalAgent MCP Server
OfficialServer Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have completely distinct purposes with no overlap. setup_browser_state handles browser authentication and state saving, while web_eval_agent performs UX/UI evaluation of web applications. An agent would never confuse these tools as they serve different phases of the workflow.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern with snake_case naming. setup_browser_state and web_eval_agent use clear action-oriented verbs followed by descriptive nouns, creating a predictable naming convention throughout the server.
Tool Count2/5With only 2 tools, this server feels severely under-equipped for its apparent purpose of web evaluation. A comprehensive web evaluation system would typically need tools for navigation, interaction simulation, accessibility testing, performance measurement, and result analysis beyond just setup and evaluation.
Completeness2/5The tool surface is severely incomplete for web evaluation. While setup and evaluation are covered, there are significant gaps: no tools for configuring evaluation parameters, managing browser sessions, analyzing results programmatically, testing specific components, or handling different evaluation methodologies. The server provides only basic endpoints without supporting the full evaluation workflow.
Average 4.1/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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
- Behavior3/5
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 the tool performs tasks and analyzes interaction flow, uses a browser (with optional headless mode), and returns evaluations with screenshots. However, it lacks details on permissions, rate limits, error handling, or whether it's read-only/destructive. The description doesn't contradict annotations (none exist).
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 appropriately sized and front-loaded with the core purpose. Each sentence adds value: purpose, prerequisites, parameter explanations, and return format. Some minor redundancy exists (e.g., repeating 'evaluate' concepts), but overall it's well-structured with zero wasted sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters with 0% schema coverage and no output schema, the description does a good job explaining inputs and outputs. It details parameter usage and describes the return format (list of text and image content with observations, issues, recommendations). For a tool with no annotations, it's reasonably complete, though could benefit from more behavioral context like error cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does 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 detailed semantics for all 3 parameters: 'url' (required, localhost URL with port examples, avoid path segments), 'task' (required, specific UX/UI aspect with examples and length guidance), and 'headless_browser' (optional, controls browser window visibility). This adds substantial meaning beyond the bare schema.
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: 'Evaluate the user experience / interface of a web application' and 'assess the quality of user experience and interface design'. It specifies the verb ('evaluate', 'assess') and resource ('web application'), but doesn't explicitly differentiate from the sibling tool 'setup_browser_state', which likely has a different function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: 'Before this tool is used, the web application should already be running locally on a port.' It also gives examples of tasks like 'test the checkout flow' or 'evaluate the navigation menu usability'. However, it doesn't explicitly state when NOT to use it or mention the sibling tool as an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
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 describes key behaviors: launches a non-headless browser for user interaction, allows login/authentication, saves browser state to a local file, and returns confirmation/error messages. However, it lacks details on permissions needed, file storage location, rate limits, or error conditions. It compensates somewhat but not fully for the annotation gap.
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 well-structured and concise: it starts with a clear purpose statement, provides specific usage guidelines, details the tool's behavior, and lists parameters and returns. Each sentence adds value without redundancy. The formatting with sections (Args, Returns) enhances readability without unnecessary length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (involving browser automation and state saving) and lack of annotations or output schema, the description does a good job covering key aspects: purpose, usage, behavior, parameters, and returns. It explains the return type ('list[TextContent]') and what it contains. However, it could improve by detailing error handling or state file management to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the 'url' parameter: 'Optional URL to navigate to upon opening the browser.' This clarifies its purpose beyond the schema's basic title. With 0% schema description coverage and only one parameter, the description adequately compensates. The mention of 'ctx' as an MCP context for progress reporting adds useful semantic insight, though it's not in the input schema.
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: 'Sets up and saves browser state for future use' with specific actions like launching a non-headless browser, allowing login/authentication, and saving state to a file. It distinguishes from the sibling 'web_eval_agent' by focusing on state setup rather than evaluation. However, it doesn't explicitly contrast with the sibling beyond implied different use cases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines: 'This tool should only be called in one scenario: 1. The user explicitly requests to set up browser state/authentication.' This clearly defines when to use it and implicitly suggests alternatives (e.g., not for general browsing). It offers strong guidance without misleading information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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