Web Explorer MCP
Server Quality Checklist
Latest release: v0.3.1
- Disambiguation5/5
The two tools have completely distinct purposes: one performs web searches and the other extracts content from a given URL. There is no overlap or ambiguity between them.
Naming Consistency5/5Both tool names follow a consistent pattern: a descriptive noun phrase followed by '_tool' (web_search_tool, webpage_content_tool). This makes the naming predictable and clear.
Tool Count3/5With only 2 tools, the server feels minimal for a 'Web Explorer' purpose. While the two tools cover search and content retrieval, the count is borderline and could benefit from additional tools like link extraction or history management.
Completeness4/5The tool set covers the core web exploration workflow: searching and fetching content. It lacks some advanced operations like extracting specific elements or managing browsing sessions, but the primary needs are met without major gaps.
Average 4.1/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community 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 is passing
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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 of behavioral disclosure. It reveals that it uses Playwright with JavaScript rendering and that content is paginated when exceeding max_chars. However, it does not explicitly state that it is read-only or disclose limitations like site-specific failures, leaving some behavioral traits implicit.
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 two sentences long, with the first sentence immediately stating the purpose. There is no redundant content; every clause contributes meaning. This is an exemplary concise structure.
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?
Combined with the 100% schema coverage and output schema, the description provides a complete functional overview: extraction, cleaning, JS rendering, and pagination. It lacks explicit guidance on when to use this tool versus the sibling and does not clarify 'clean', but these are minor gaps given the existing structured context.
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 schema covers all four parameters with descriptions, so the high-coverage baseline applies. The description adds a note about pagination behavior, but this mostly restates the schema's 'pagination is applied to main_content only' without deepening parameter understanding. Therefore, a score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with a specific verb+resource: 'Extract and clean webpage content for a provided URL.' This clearly distinguishes it from the sibling web_search_tool, which searches rather than fetches a known URL. The wording is unambiguous and informative.
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 gives clear context that this tool is for extracting content from a specific URL, implying use when a URL is already known. It does not explicitly state when not to use it or mention alternatives, but the distinction from web_search_tool is clear enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It discloses the result structure, pagination support, and graceful error handling ('returns them in the response rather than raising exceptions'). It does not mention rate limits or authentication, but for a local search tool these are minor omissions. It provides valuable behavioral context.
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 two sentences with no filler. The first sentence gives the core purpose, and the second adds essential behavioral details (result fields, pagination, error handling). It is front-loaded and efficiently worded.
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?
The tool has an output schema, so the description needn't detail return values. It covers the core function, result fields, pagination, and error behavior. It lacks explicit guidance on when to choose this over webpage_content_tool, but that is covered under usage guidelines. Overall, it is a well-rounded description for a search tool.
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 already provides 100% coverage for query, page, and page_size. The description mentions pagination support generally, which reaffirms the page/page_size purpose, but adds no specific parameter semantics beyond what the schema already states. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Perform web search using SearxNG instance' – a specific verb and resource. It clearly states the tool searches the web and returns structured results (title, description, URL), distinguishing it from the sibling webpage_content_tool which fetches page content. No ambiguity.
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 clearly implies the tool is for web searches and provides context about results and pagination, but it does not explicitly mention when to use it over webpage_content_tool or state any exclusion criteria. The use case is clear, but alternative guidance is not explicit.
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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