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pzalutski-pixel

NetLens

by pzalutski-pixel
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Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.3

  • Disambiguation5/5

    web_search and web_fetch have clearly distinct purposes: one finds relevant URLs via search, the other retrieves full page content. The descriptions explicitly differentiate them and explain their complementary workflow, eliminating any ambiguity.

    Naming Consistency5/5

    Both tools follow the consistent verb_noun pattern (web_search, web_fetch), making the naming predictable and easy to understand.

    Tool Count3/5

    Only 2 tools is minimal, but for a narrow focus on web searching and fetching, it's plausible. However, it feels slightly thin for a general-purpose server, as many use cases might require additional tools.

    Completeness2/5

    The tool surface covers only search and fetch, lacking tools for pagination, saving results, or handling multiple search engines. This leaves obvious gaps for a comprehensive web research workflow, potentially causing agent failures.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 18 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    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.

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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?

    Annotations already declare readOnlyHint and openWorldHint. The description adds significant behavioral context: parsing from HTML endpoints, bot-bypassing, returning one page of ~10 results, and engine fallback behavior. No contradictions.

    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 concise and well-structured: purpose first, then output type, then limitations, then workflow. Every sentence adds value, and the length is appropriate for the tool's complexity.

    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?

    Given the complexity (6 parameters, 100% schema coverage, no output schema, annotations present), the description covers all key aspects: return format, pagination limits, engine behavior, and workflow. It is complete for the agent to use the tool effectively.

    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 coverage is 100%, so baseline is 3. The description does not add new parameter semantic details beyond what the schema provides, though it does explain engine behavior and page limitations implicitly. No additional value beyond schema.

    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 it searches the web and returns links (title, URL, snippet). It distinguishes from the sibling tool web_fetch by specifying that it returns links not summaries, and explicitly recommends following up with web_fetch for full content.

    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 explicit guidance: use web_search to find pages, then web_fetch to read them. It also warns against expecting deep pagination and suggests refining the query. However, it does not explicitly state when not to use it or list alternative tools beyond web_fetch.

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

  • Behavior5/5

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

    The description reveals important behavioral traits not covered by annotations: uses browser-like headers, converts HTML to Markdown locally, selects main content region, and explains modes. No contradiction with annotations.

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

    Conciseness4/5

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

    The description is well-structured and information-dense, starting with the core purpose and then detailing modes and limitations. It could be slightly more concise, but each sentence adds value.

    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?

    Given the tool's 5 parameters and no output schema, the description covers all aspects: modes, parameter behavior, limitations, and use cases. It is thorough enough for an AI agent to use correctly.

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

    Parameters4/5

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

    While the input schema describes all parameters, the description adds context such as default modes, the purpose of each mode, and the interplay between parameters (e.g., using 'outline' before 'section'). This enhances understanding beyond the schema.

    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 fetches any web page and returns full content as clean Markdown, distinguishing it from the sibling 'web_search' which likely returns search results.

    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 explains when to use the tool (fetching full pages, bypassing bot filters) and its limitations (cannot solve JS/Cloudflare challenges or CAPTCHAs). However, it does not explicitly compare to the sibling tool 'web_search'.

    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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