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marcopesani

serper-search-scrape-mcp-server

by marcopesani
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: google_search is for performing web searches and retrieving search results, while scrape is for extracting content from a specific webpage. There is no overlap in functionality, making it easy for an agent to choose the right tool for each task.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern using snake_case with descriptive, action-oriented names (google_search and scrape). The naming is straightforward and predictable, with no deviations or mixed conventions.

    Tool Count3/5

    With only 2 tools, the server feels thin for a search-and-scrape domain, as it might lack additional utilities like batch processing, advanced filtering, or result summarization. However, the core functions are covered, making it borderline but functional.

    Completeness4/5

    The tools cover the essential workflows for search and scraping: performing searches and extracting webpage content. Minor gaps exist, such as no tool for managing search history or handling pagination, but agents can work around these with the provided tools.

  • Average 2.9/5 across 2 of 2 tools scored.

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

    • 0 of 1 community issues answered or closed 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 the tool 'retrieves rich results' but lacks critical details: it doesn't specify rate limits, authentication requirements, error handling, pagination behavior, or whether it's read-only or mutative. For a complex search tool with 19 parameters, this is a significant gap in transparency.

    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 appropriately concise with two sentences that efficiently convey the core functionality. It's front-loaded with the main purpose and follows with specific result types. There's no wasted verbiage, though it could potentially benefit from more structured formatting given the tool's complexity.

    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 (19 parameters), lack of annotations, and absence of an output schema, the description is incomplete. It doesn't explain what the return values look like, how results are structured, error conditions, or performance characteristics. For a search tool with rich parameter options, users need more context about what to expect from the operation.

    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 all 19 parameters well-documented in the input schema. The description doesn't add any parameter-specific information beyond what's already in the schema. According to guidelines, when schema coverage is high (>80%), the baseline score is 3 even without parameter details in the description.

    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 performs web searches via Serper API and retrieves rich results, specifying organic search results, people also ask, related searches, and knowledge graph. It uses specific verbs ('perform', 'retrieve') and identifies the resource (web searches). However, it doesn't explicitly differentiate from the sibling 'scrape' tool, 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 the sibling 'scrape' tool or other alternatives. It mentions the types of results retrieved but offers no context about appropriate use cases, prerequisites, or exclusions. This leaves the agent without clear decision-making criteria.

    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 states what the tool retrieves (text, markdown, JSON-LD, head metadata) but lacks critical behavioral details such as rate limits, authentication needs, error handling, or whether it performs destructive actions (e.g., modifying data). For a web scraping tool with no annotation coverage, this is a significant gap in transparency.

    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 and front-loaded in a single sentence, efficiently stating the core functionality without unnecessary details. Every part earns its place, though it could be slightly more structured (e.g., separating outputs). It avoids redundancy and is appropriately sized for the tool's complexity.

    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 (web scraping with two parameters), no annotations, and no output schema, the description is partially complete. It covers what the tool retrieves but misses behavioral aspects like rate limits or error handling. Without an output schema, it should ideally describe return values more explicitly, but it does list the types of content retrieved, providing some 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 'includeMarkdown'). The description adds no additional meaning beyond the schema—it doesn't explain parameter interactions, constraints, or usage examples. With high schema coverage, the baseline score of 3 is appropriate as the schema handles 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: 'scrape a webpage and retrieve the text and, optionally, the markdown content. It will retrieve also the JSON-LD metadata and the head metadata.' This specifies the verb (scrape), resource (webpage), and outputs (text, markdown, JSON-LD, head metadata). However, it doesn't explicitly differentiate from the sibling tool 'google_search', which likely serves a different purpose (searching vs. scraping).

    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 the sibling tool 'google_search' or any other scraping-related tools, nor does it specify prerequisites, contexts, or exclusions for usage. This leaves the agent without clear direction on tool selection.

    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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