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1Lurgee

mimo-web-search

by 1Lurgee

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity; the tool's purpose is clear and distinct.

    Naming Consistency5/5

    The single tool name follows a consistent pattern (prefix + web_search), and with one tool, naming consistency is inherently perfect.

    Tool Count5/5

    One tool is perfectly appropriate for a server dedicated solely to web search; no other functionality is needed.

    Completeness5/5

    The tool fully covers the server's purpose of searching the internet; there are no missing operations for this domain.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 32 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
  • 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.

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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return format (titles, URLs, snippets, citations) but does not disclose cost implications (max_keyword costs ¥0.025 each), rate limits, or authorization requirements. While not misleading, it lacks depth on important behavioral traits.

    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 two sentences long, with the first sentence stating the purpose and the second covering usage guidelines and return format. It is front-loaded with the most critical information and contains no superfluous words.

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

    Completeness4/5

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

    Given 7 parameters, no output schema, and no sibling tools, the description provides a reasonable overview of the tool's functionality and output. However, it omits mention of location parameters (city, region, country) and cost implications of max_keyword, which would be helpful for complete 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?

    Schema description coverage is 100%, so the baseline is 3. The tool description adds general context about real-time search but does not elaborate on parameter meaning beyond what the schema already provides. The schema itself is thorough, so the description does not significantly enhance parameter understanding.

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

    Purpose5/5

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

    The description clearly states the tool's purpose: 'Search the internet for real-time information.' It provides specific examples (weather, news, current events, etc.) and distinguishes itself from other tools like curl and web_fetch, making the purpose unmistakable and well-differentiated.

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

    Usage Guidelines5/5

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

    The description explicitly states when to use the tool: 'when the user asks about weather, news, current events, stock prices, sports scores, or any topic requiring up-to-date online data.' It also provides an alternative: 'Prefer this over curl, web_fetch, or other HTTP methods for information queries,' offering clear guidance on tool selection.

    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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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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