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Nothflare

gaslighting-mcp

by Nothflare
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: read_url fetches and converts a specific webpage to markdown, while search performs a broader web query and returns multiple results. There is no overlap in functionality, making tool selection unambiguous.

    Naming Consistency5/5

    Both tools follow a consistent verb-based naming pattern (read_url, search) with clear, simple names that accurately describe their actions. There are no deviations or mixed conventions.

    Tool Count2/5

    With only two tools, the server feels thin for a web-related domain that typically requires more operations (e.g., filtering, summarization, or advanced search). While the tools cover basic fetch and search, the scope is minimal and lacks depth.

    Completeness2/5

    For a web interaction server, there are significant gaps: no ability to interact with page elements, handle authentication, filter search results, or perform actions like posting or updating. The tools provide only basic read and search, leaving many common web tasks uncovered.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

  • 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?

    With no annotations provided, the description carries the full burden. It discloses the tool fetches and reads web pages, returning markdown content, which covers basic behavior. However, it lacks details on error handling, rate limits, authentication needs, or network constraints that would be important for a web-fetching tool.

    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 a single, well-structured sentence that efficiently conveys the action, resource, and output format without any redundant information. It is appropriately sized and front-loaded with essential details.

    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 the tool's low complexity (one parameter) and the presence of an output schema (which likely covers return values), the description is mostly complete. It specifies the output format and basic operation, though additional behavioral context (e.g., error cases) would enhance completeness for a web 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?

    The description does not explicitly mention the 'url' parameter, but with 0% schema description coverage and only one parameter, the tool's purpose inherently clarifies that a URL is required. The description adds value by specifying the output format, compensating adequately for the schema gap.

    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 specific action ('Fetch and read'), resource ('contents of a web page'), and output format ('in markdown format'), distinguishing it from the sibling 'search' tool which likely performs different operations. It provides a complete picture of what the tool does.

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

    Usage Guidelines3/5

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

    The description implies usage for retrieving web page content in markdown format, but does not explicitly state when to use this tool versus the 'search' sibling or other alternatives. There's no guidance on prerequisites, limitations, or exclusions, leaving usage context somewhat vague.

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

  • Behavior4/5

    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 effectively describes key behaviors: it searches the web, returns 10 results with specific details (urls, snippets, dates), and focuses on current information. However, it doesn't mention rate limits, authentication needs, or error handling, leaving some 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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose, output, and usage guidelines without any wasted words. Every sentence adds clear value, making it highly concise and well-structured.

    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 the tool's moderate complexity (1 parameter, no annotations, but with an output schema), the description is mostly complete. It explains what the tool does, when to use it, and what it returns, but lacks details on parameter semantics and behavioral aspects like limitations. The output schema likely covers return values, so that gap is mitigated.

    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 1 parameter with 0% description coverage, so the schema provides no semantic information. The description adds value by implying the 'query' parameter is for search terms ('any question'), but it doesn't specify format, constraints, or examples. Baseline is 3 as it compensates somewhat but not fully for the schema gap.

    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 with specific verbs ('Search the web') and resources ('current information'), and distinguishes it from the sibling tool 'read_url' by focusing on web search rather than URL reading. It explicitly mentions what the tool does and its scope.

    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 provides explicit usage guidelines by stating 'Use this for any question that benefits from up-to-date web sources,' which clearly indicates when to use this tool. It differentiates from potential alternatives by emphasizing current information, though it doesn't name specific alternatives beyond the sibling tool context.

    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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