Web Scout MCP Server
Server Quality Checklist
Latest release: v1.5.5
- Disambiguation5/5
The two tools have completely distinct purposes: DuckDuckGoWebSearch performs web searches to find URLs, while UrlContentExtractor extracts content from specific URLs. There is no overlap in functionality or ambiguity about when to use each tool.
Naming Consistency4/5Both tools use descriptive, multi-word names that clearly indicate their function. While not following a strict verb_noun pattern, they maintain readability and consistency in style. The minor deviation from perfect pattern consistency prevents a score of 5.
Tool Count2/5With only 2 tools for a web search and content extraction server, the surface feels thin and incomplete. A typical web search server would benefit from additional tools like advanced search filters, result pagination, or content analysis utilities to provide more comprehensive coverage.
Completeness2/5While the basic search-to-extract workflow is covered, there are significant gaps in the web search domain. Missing operations include search result filtering, handling pagination, saving/search history, content summarization, or image/video search capabilities that would be expected in a complete web search toolkit.
Average 2.9/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 2 of 2 community issues answered or closed in the last 6 months
- 6 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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This repository includes a glama.json configuration file.
This server has been verified by its author.
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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
- 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 mentions that the tool 'returns a well-structured list of findings' and defaults maxResults to 10, but lacks details on rate limits, authentication needs, error handling, or what 'well-structured' entails. For a search tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.
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 with two sentences that efficiently cover the tool's function and parameters. It is front-loaded with the core purpose, though the second sentence could be slightly more streamlined by avoiding repetition of 'Input'.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and parameters but lacks details on output format, error cases, and behavioral traits. Without annotations or an output schema, more context on what 'well-structured list' means would improve completeness.
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%, with both parameters (query and maxResults) fully documented in the schema. The description adds minimal value beyond the schema: it reiterates that query is for 'keywords, question, or topic' and notes the default for maxResults, which is already in the schema. Baseline 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.
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: 'Initiates a web search query using the DuckDuckGo search engine and returns a well-structured list of findings.' This specifies the verb ('initiates a web search'), resource ('web search query'), and engine ('DuckDuckGo'), distinguishing it from the sibling tool UrlContentExtractor. However, it doesn't explicitly contrast with the sibling beyond mentioning the engine.
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 like UrlContentExtractor. It mentions the query input and maxResults default but offers no context about appropriate use cases, prerequisites, or exclusions. Usage is implied through parameter descriptions but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
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 states the tool fetches and extracts content but lacks details on potential issues like rate limits, authentication needs, error handling, or what 'extracts content' entails (e.g., text, HTML, metadata). This leaves significant gaps in understanding its behavior.
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 stated first. Both sentences are relevant, but the second sentence could be slightly more concise by combining the single and multiple URL explanations without repetition.
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 lack of annotations and output schema, the description is incomplete for a tool that performs web content extraction. It doesn't explain what 'extracts content' means in terms of output format, potential limitations (e.g., JavaScript-rendered content), or error scenarios, leaving 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?
The schema description coverage is 100%, so the schema already fully documents the 'url' parameter as a string or array of URIs. The description adds minimal value by restating this in plain language without providing additional context, such as URL format constraints or performance implications of array inputs, aligning with the baseline score 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 specific verbs ('fetches and extracts content') and resource ('from a given webpage URL'), making it easy to understand what it does. However, it doesn't explicitly differentiate from its sibling tool DuckDuckGoWebSearch, which likely serves a different search-oriented purpose rather than direct content extraction.
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, such as DuckDuckGoWebSearch. It mentions the ability to handle single or multiple URLs but doesn't clarify scenarios where one might prefer this over other tools or when it's inappropriate to use.
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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