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blueewhitee

agent-web-search-mcp

by blueewhitee
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is zero chance of selecting the wrong tool. The tool's purpose is clearly defined as web search.

    Naming Consistency5/5

    The single tool name 'web_search' follows a clear verb_noun pattern, but there are no other tools to compare consistency.

    Tool Count2/5

    The server has only one tool, which falls below the typical minimum for a useful server. While search can be a single operation, the rubric considers 1 tool too few.

    Completeness5/5

    For the stated purpose of web search, the tool provides comprehensive functionality including content fetching and ranking. There are no obvious missing operations within its scope.

  • Average 4.6/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

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  • 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.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    The description discloses substantial behavioral details beyond the annotations: it queries multiple engines, fetches full page content, scrubs prompt injections, chunks by semantic boundaries, and ranks by cosine similarity. This gives the agent a realistic expectation of processing scope and security measures, far exceeding the minimal readOnly/idempotent hints from annotations.

    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 efficiently structured: a one-sentence purpose statement, a compact process overview, a clear usage trigger, and a concise return-format note. No word is wasted, and it is appropriately front-loaded with the primary action.

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

    Completeness5/5

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

    Despite the tool's complexity, the description covers the core function, internal processing steps, usage context, and output structure. The presence of an output schema further fills in return details, so the description is complete enough for an agent to select and invoke the tool confidently.

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

    Parameters2/5

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

    The description does not explicitly mention the 'query' or 'render_js' parameters, despite schema description coverage being 0%. While the schema itself provides robust descriptions, the fail here is that the tool description adds no direct value for parameter selection, failing to compensate for the low coverage as required.

    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 opens with 'Search the web and return ranked, content-rich results,' which clearly specifies the verb (search), resource (web), and output format (ranked results). It further details the multi-step process, making it unambiguous and distinguishing it from generic search tools.

    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: 'Use this when the user asks about current information, facts, documentation, or anything that requires up-to-date web results.' This provides clear, actionable guidance for an AI agent, even without sibling tools to compare against.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

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