Skip to main content
Glama
PhialsBasement

MCP Web Research Server

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.2

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: general web search vs. academic search vs. page content extraction vs. visual capture. No overlaps in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case (search_google, search_scholar, take_screenshot, visit_page), making them predictable.

    Tool Count5/5

    Four tools is well-scoped for a web research server—enough to cover core tasks without unnecessary complexity.

    Completeness4/5

    The set covers search (web and academic), page visiting, and screenshot capabilities. Missing a tool for managing search history or saving results, but core research workflows are supported.

  • Average 4/5 across 4 of 4 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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 should disclose behavioral traits. It only states that it returns structured data with certain fields, but does not mention whether the tool is read-only, has rate limits, requires authentication, or any side effects. This lack of transparency is a gap.

    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 concise with three sentences, front-loading the core action. It is efficient and easy to parse, though slightly more structure (e.g., bullet points) could improve readability.

    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?

    For a simple search tool with one parameter and no output schema, the description covers purpose, usage context, and return structure. However, it lacks details on pagination, result limits, or sorting, which could be useful for an agent.

    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 already provides a description for the only parameter ('Academic search query'). The tool description does not add any additional meaning beyond what the schema states, so baseline 3 applies.

    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 action ('Searches Google Scholar') and specifies the resource ('academic papers and scholarly articles'). It distinguishes itself from sibling tools like search_google by focusing on academic 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 on when to use this tool (researching scientific topics, peer-reviewed research, etc.). It does not explicitly mention when not to use or contrast with alternatives, but the focus on academic literature implicitly differentiates it from general web searches.

    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?

    No annotations exist, and the description lacks details about side effects, image format, or behavior if no page is loaded, leaving significant behavioral gaps.

    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 efficient sentences, front-loaded with the verb 'Captures', and every word adds value without redundancy.

    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?

    While the purpose is clear, the description omits details about output (e.g., format, full-page vs viewport), which would help completeness for a simple tool.

    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?

    With zero parameters, baseline is 4; the description correctly implies no configuration is needed.

    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 captures a visual image of the currently loaded webpage, and it is distinct from sibling tools like search_google, search_scholar, and visit_page.

    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 use cases (preserve visual info, analyze layouts, document state) but does not explicitly mention when not to use it or alternatives, though siblings are different.

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

  • Behavior3/5

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

    No annotations provided, so description must cover behavioral aspects. States return format (titles, URLs, snippets) but omits details like rate limits, query length limits, pagination, or error handling. Basic transparency but with notable gaps.

    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?

    Three sentences, no unnecessary words, front-loaded with purpose. Every sentence adds value: what it does, when to use, what it returns.

    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 a simple tool with one parameter and no output schema, the description covers primary purpose, use cases, and return format. Lacks details like result count limits or error scenarios, but adequate for basic usage.

    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?

    Only one parameter (query) with 100% schema description coverage (minimal: 'Search query'). The tool description adds no further parameter-specific meaning, only repeating use cases. Baseline score of 3 is appropriate.

    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?

    Clearly states 'Performs a web search using Google' with specific use cases (current information, news, websites, general knowledge). Distinguishes from sibling tools like search_scholar (academic) and visit_page (browsing).

    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?

    Provides clear guidance on when to use: 'research topics, find recent information, or gather data from the web.' Does not explicitly mention when not to use or alternatives, but context implies a general web search tool.

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

  • Behavior3/5

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

    No annotations provided, so description must cover behavioral traits. It mentions extracting content and screenshots but omits details like error handling, dynamic content, rate limits, or side effects.

    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?

    Three sentences, first covers core action and options, next two provide guidance. No unnecessary words, well-structured and front-loaded.

    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?

    For a simple tool with 2 params and no output schema, description adequately covers purpose and usage but lacks details on output format (e.g., how page content is returned) and potential limitations.

    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?

    Schema coverage is 100% with adequate descriptions. Description adds context for takeScreenshot ('option to capture a screenshot') but does not clarify URL format or restrictions 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 action (navigates to URL, extracts content, optionally screenshots) and differentiates from sibling tools like search_google (which finds pages) and take_screenshot (which only captures images).

    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?

    Explicitly recommends use for in-depth research after search, and outlines scenarios (analyze pages, read articles, verify info), providing clear context for when to use.

    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.

Card Badge

mcp-webresearch-stealthified MCP server

Copy to your README.md:

Score Badge

mcp-webresearch-stealthified MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/PhialsBasement/mcp-webresearch-stealthified'

If you have feedback or need assistance with the MCP directory API, please join our Discord server