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jevy

MCP Web Research Server

by jevy
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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: search_google finds web pages, visit_page loads and extracts content from a specific URL, and take_screenshot captures visual data from the current page. An agent can easily differentiate these functions without confusion.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case: search_google, take_screenshot, and visit_page. This uniformity makes the set predictable and easy to understand at a glance.

    Tool Count3/5

    With only 3 tools, the server feels thin for a 'Web Research Server' scope, as it lacks operations like navigating pages, interacting with elements, or managing browser sessions. While the tools cover basic functions, the count is borderline low for comprehensive web research tasks.

    Completeness2/5

    There are significant gaps in the tool surface for web research: no navigation tools (e.g., go_back, click_element), no interaction capabilities (e.g., fill_form, scroll), and no session management. This incomplete coverage will likely cause agent failures in complex research workflows.

  • Average 3/5 across 3 of 3 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.

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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 states what the tool does but doesn't describe any behavioral traits such as rate limits, authentication needs, result format, pagination, or whether it's a read-only operation. This leaves significant gaps for an agent to understand how to interact with it effectively.

    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 extremely concise with just four words, front-loaded with the core action, and contains zero wasted information. Every word earns its place by directly contributing to understanding the tool's purpose.

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

    Completeness2/5

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

    Given the tool's complexity (a search operation with potential behavioral nuances) and the lack of annotations and output schema, the description is incomplete. It doesn't cover what the tool returns, how results are structured, or any operational constraints, making it insufficient for an agent to use the tool confidently without additional 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 schema description coverage is 100%, with the single parameter 'query' fully documented in the schema. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain query syntax, examples, or constraints), so it meets the baseline for adequate but unenhanced parameter documentation.

    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 verb ('Search') and resource ('Google') with the specific action ('for a query'), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'visit_page' or 'take_screenshot' that might also involve web interactions, so it doesn't reach the highest score.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives like 'visit_page' or 'take_screenshot'. It doesn't mention any prerequisites, exclusions, or specific contexts where this search tool is preferred over direct page visits or other methods.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'extract content' but doesn't specify what content is extracted (e.g., HTML, text, metadata), how errors are handled, rate limits, authentication needs, or whether the operation is idempotent. The description is too vague about the tool's actual behavior beyond the basic action.

    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 extremely concise at just 7 words, front-loading the core functionality with zero wasted language. Every word earns its place by communicating essential information about the tool's action and target.

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

    Completeness2/5

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

    For a tool with no annotations, no output schema, and behavioral complexity (web scraping can involve many edge cases), the description is incomplete. It doesn't address what content is extracted, error handling, performance characteristics, or relationship to sibling tools. The agent would need to guess about important operational aspects.

    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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any meaningful context about parameter usage beyond what's in the schema (e.g., when to use takeScreenshot, URL format requirements). Baseline 3 is appropriate when the schema does the heavy lifting.

    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 specific verbs ('visit' and 'extract') and identifies the resource ('webpage content'). It distinguishes itself from sibling 'take_screenshot' by focusing on content extraction rather than just visual capture, though it doesn't explicitly differentiate from 'search_google' which might have overlapping functionality.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives like 'search_google' or 'take_screenshot'. It doesn't mention prerequisites, constraints, or typical use cases, leaving the agent to infer usage from the tool name and parameters alone.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('take a screenshot') but lacks details on permissions, file format, storage location, or any side effects. This is a significant gap for a tool that performs a capture operation without any structured safety hints.

    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, efficient sentence that directly states the tool's purpose without any wasted words. It is front-loaded and appropriately sized for a simple tool with no parameters, making it easy for an agent to parse quickly.

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

    Completeness2/5

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

    Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the screenshot returns (e.g., image data, file path) or any behavioral nuances, which is inadequate for a tool that performs a capture action without structured context.

    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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter details, as there are none to explain, aligning with the baseline for zero parameters.

    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 action ('take') and resource ('screenshot of the current page'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'search_google' or 'visit_page', which are distinct browsing actions rather than screenshot operations, so it lacks explicit sibling differentiation.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives or in what context it should be invoked. It implies usage for capturing the current page but offers no exclusions, prerequisites, or comparisons to other tools, leaving the agent to infer usage based on the tool name alone.

    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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