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SayreBlades

MCP Web Tools

by SayreBlades
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: web_fetch retrieves the content of a specific URL, while web_search queries a search engine for results. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent 'web_' prefix and snake_case naming pattern (web_fetch and web_search). This predictable structure enhances readability and usability for agents.

    Tool Count2/5

    With only 2 tools, the server feels thin for a 'Web Tools' domain. While fetch and search are core operations, the scope suggests potential for more functionality (e.g., parsing, navigation, or analytics), making the toolset appear incomplete.

    Completeness3/5

    The tools cover basic web interaction (fetching content and searching), but there are notable gaps. For a web tools server, missing operations like navigating links, extracting specific data, or handling web forms limit its utility for complex agent workflows.

  • Average 3.4/5 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

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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",
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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

  • Behavior2/5

    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 mentions the tool returns 'raw text content' and hints at use cases, but fails to disclose critical traits like error handling (e.g., for invalid URLs), authentication needs, rate limits, or performance considerations. This leaves significant gaps for a tool that interacts with external web resources.

    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, consisting of two concise sentences that directly state the tool's function and its return value. Every sentence earns its place by providing essential information without redundancy, making it efficient and well-structured.

    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?

    Given the tool's moderate complexity (fetching web content) and the absence of annotations and output schema, the description is partially complete. It covers the basic purpose and return type but lacks details on behavioral aspects, error handling, and output structure. This makes it adequate as a minimum viable description but with clear gaps in 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?

    The input schema has 100% description coverage, clearly documenting both parameters ('url' and 'timeout'). The description adds no additional meaning beyond the schema, as it doesn't elaborate on parameter usage or constraints. According to the rules, with high schema coverage (>80%), the baseline is 3, which applies here since the description doesn't compensate with extra insights.

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

    Purpose4/5

    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 a specific verb ('fetch') and resource ('content of a web page'), and distinguishes it from sibling 'web_search' by focusing on retrieving raw text from a given URL rather than searching. However, it doesn't explicitly differentiate from potential siblings like 'web_scrape' or 'web_metadata', keeping it at 4 instead of 5.

    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 by stating it's 'suitable for reading articles and documentation,' suggesting contexts like content extraction. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'web_search' for broader queries) or any exclusions (e.g., not for dynamic pages), leaving usage somewhat inferred rather than clearly defined.

    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 are provided, so the description carries the full burden of behavioral disclosure. It mentions the search engine (DuckDuckGo) and return format, but fails to disclose critical traits like rate limits, authentication needs, error handling, or whether it's a read-only operation. For a tool with no annotations, this leaves significant gaps in understanding its behavior and constraints.

    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 front-loaded and highly concise, consisting of two sentences that efficiently convey the tool's purpose and output. Every sentence earns its place by providing essential information without redundancy or unnecessary details, making it easy for an agent to quickly grasp the tool's function.

    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?

    Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and output but lacks details on behavioral traits, error cases, or integration with siblings. Without annotations or output schema, more context on limitations or usage patterns would improve completeness for agent decision-making.

    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 schema description coverage is 100%, so the schema already documents all parameters (query, max_results, region) with descriptions and defaults. The description does not add any meaning beyond what the schema provides, such as examples or usage tips for parameters. Baseline 3 is appropriate as the schema handles the heavy lifting, but no extra value is added.

    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 ('Search the web using DuckDuckGo') and resource ('web'), distinguishing it from sibling 'web_fetch' which likely fetches specific URLs rather than performing searches. It explicitly mentions what it returns ('list of search results with titles, URLs, and snippets'), making the purpose unambiguous and distinct.

    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 web searches but does not explicitly state when to use this tool versus the sibling 'web_fetch' or other alternatives. It provides basic context (searching the web) but lacks guidance on exclusions, prerequisites, or specific scenarios where this tool is preferred over others, leaving some ambiguity for the agent.

    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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