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MissionSquad

@missionsquad/mcp-searxng-puppeteer

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by MissionSquad
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JavaScript
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Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: 'get_url_content' retrieves content from a specific URL, while 'web_search' performs broad searches across the web. There is no overlap or ambiguity between them, making it easy for an agent to select the appropriate tool based on the task.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern ('get_url_content' and 'web_search'), using snake_case throughout. This predictability aids in understanding and usage without any deviations or mixed conventions.

    Tool Count2/5

    With only 2 tools, the server feels thin for its apparent scope of web search and content retrieval. While the tools cover basic operations, the count is too low for a comprehensive web interaction toolset, lacking features like advanced search filters, content analysis, or URL management.

    Completeness2/5

    The tool surface is severely incomplete for a web search and content retrieval domain. It provides search and URL fetching but lacks essential operations such as filtering search results, handling pagination, analyzing content (e.g., summarization), or managing search history, which are common in similar toolsets.

  • Average 3.5/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • No commit activity data available
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to 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

  • 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 retrieves content but lacks details on behavioral traits like error handling (e.g., for invalid URLs), authentication needs, rate limits, or output format (e.g., text, HTML). This leaves significant gaps for an agent to understand how the tool behaves in practice.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is appropriately sized and front-loaded: the first sentence states the core purpose, and the second provides usage context. Both sentences earn their place by adding value without redundancy, making it efficient and easy to parse for an agent.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is moderately complete. It covers purpose and usage but lacks details on behavioral aspects like error handling or output format, which are important for a content retrieval tool. Without annotations or output schema, more context would improve completeness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 100% description coverage, with the 'url' parameter documented as 'URL'. The description adds no additional meaning beyond this, as it doesn't specify URL format constraints (e.g., must be valid HTTP/HTTPS) or usage examples. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra insights.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Get the content of a URL' specifies the verb (get) and resource (URL content). It distinguishes from the sibling 'web_search' by focusing on retrieving content from a specific URL rather than searching the web. However, it doesn't specify the exact nature of the content (e.g., HTML, text, 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 Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear usage context: 'Use this for further information retrieving to understand the content of each URL' indicates when to use it—for analyzing URL content. It implicitly distinguishes from 'web_search' by focusing on specific URLs rather than search queries. However, it lacks explicit exclusions or alternatives, such as when not to use it (e.g., for invalid URLs or rate-limited sites).

    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 mentions the API (SearXNG) and general use cases but lacks critical details like rate limits, authentication needs, error handling, or response format. For a tool with no annotations, 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is front-loaded with the core purpose and efficiently lists ideal use cases in two concise sentences. Every sentence adds value without redundancy, making it easy to scan and understand quickly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (6 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and usage well but lacks behavioral details and output information, which are crucial for effective tool invocation in this context.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as examples or contextual usage tips. This meets the baseline for high schema coverage but doesn't enhance understanding.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool performs web searches using the SearXNG API, specifying it's for general queries, news, articles, and online content. It distinguishes from the sibling tool 'get_url_content' by focusing on search rather than content retrieval, though the distinction could be more explicit.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear usage contexts: broad information gathering, recent events, and when diverse web sources are needed. It implies when to use this tool but doesn't explicitly state when to use the sibling 'get_url_content' or provide exclusions, which keeps it from a perfect score.

    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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  • 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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