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qpd-v

MCP Web Research Server

by qpd-v
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TypeScript
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: deep_research is for comprehensive topic analysis, parallel_search is for multi-query search execution, and visit_page is for single-page content extraction. There is no overlap in functionality, making tool selection unambiguous for an agent.

    Naming Consistency5/5

    All tools follow a consistent snake_case verb_noun pattern (deep_research, parallel_search, visit_page) with clear action-oriented names. The naming scheme is predictable and readable throughout the set.

    Tool Count3/5

    With only 3 tools, the set feels thin for a 'Web Research Server' domain, lacking operations like search filtering, result summarization, or citation management. While the tools cover core actions, the count is borderline minimal for comprehensive research workflows.

    Completeness3/5

    The tools cover basic research steps (search, page access, analysis), but there are notable gaps: no ability to refine searches, save results, compare sources, or handle authentication. This limits agents to a linear workflow without advanced research capabilities.

  • Average 2.9/5 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 mentions 'content extraction and analysis' but fails to detail critical aspects such as execution time, resource usage, error handling, or output format. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.

    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 unnecessary words. It's front-loaded with the core action and avoids redundancy, making it highly concise and well-structured for quick comprehension.

    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 complexity of a 'deep research' tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'deep research' entails, how results are returned, or any behavioral constraints, leaving the agent with inadequate information for effective use in a broader 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?

    Schema description coverage is 100%, meaning all parameters are documented in the schema. The description adds no additional semantic context about parameters beyond implying 'deep research' involves branching and depth. This meets the baseline for high schema coverage but doesn't enhance understanding of parameter roles or interactions.

    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 as 'Perform deep research on a topic with content extraction and analysis,' which specifies the verb (perform deep research) and resource (topic) with additional capabilities (content extraction and analysis). However, it doesn't explicitly differentiate from sibling tools like 'parallel_search' or 'visit_page,' which prevents a perfect 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 'parallel_search' or 'visit_page.' It lacks any context about appropriate scenarios, prerequisites, or exclusions, leaving the agent with minimal direction for tool selection.

    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 'parallel' execution but doesn't explain what that entails operationally (e.g., concurrency limits, error handling, or performance implications). It also omits critical details like authentication needs, rate limits, or whether this is a read-only operation.

    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 a single, clear sentence that directly states the tool's function. There is no wasted language or unnecessary elaboration, making it easy to parse and understand at a glance.

    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 insufficient for a tool that performs parallel operations. It doesn't address key behavioral aspects like error handling, result format, or limitations of parallel execution, leaving significant gaps in understanding how 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?

    The input schema has 100% description coverage, providing clear documentation for both parameters. The description adds minimal value beyond the schema by implying the tool handles multiple queries simultaneously, but doesn't elaborate on parameter interactions or usage nuances beyond what's already in the structured data.

    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 ('perform') and resource ('Google searches'), and specifies the parallel execution aspect. However, it doesn't explicitly differentiate from sibling tools like 'deep_research' or 'visit_page', which might have overlapping search functionality but different approaches.

    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 'deep_research' or 'visit_page'. It doesn't specify scenarios where parallel searching is preferred over sequential or deeper research methods, leaving the agent to infer usage context.

    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 'visit a webpage and extract its content', which implies a read operation, but doesn't specify details like authentication needs, rate limits, error handling, or what 'extract content' entails (e.g., HTML, text, metadata). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

    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 function without unnecessary words. It is front-loaded with the core action ('visit a webpage') and purpose ('extract its content'), making it easy to understand quickly. Every part of the sentence earns its place by conveying essential information.

    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 web interaction tool with potential behavioral nuances) and the lack of annotations and output schema, the description is incomplete. It doesn't cover what 'extract content' means in terms of output format, error cases, or limitations. For a tool that interacts with external webpages, more context is needed to ensure proper 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?

    The input schema has 100% description coverage, with the 'url' parameter clearly documented as 'URL to visit'. The description adds no additional meaning beyond this, as it doesn't elaborate on URL format constraints or extraction specifics. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to given the schema's clarity.

    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 ('visit') and resource ('webpage'), and specifies the action ('extract its content'). However, it doesn't differentiate this tool from potential sibling tools like 'deep_research' or 'parallel_search', which might have overlapping functionality. The description is not tautological but lacks sibling distinction.

    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. It doesn't mention any context, prerequisites, or exclusions, and doesn't reference sibling tools like 'deep_research' or 'parallel_search' that might be related. Usage is implied only by the tool's name and description, with no explicit guidelines.

    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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