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starnikovoleg

serpens-mcp

by starnikovoleg
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation4/5

    Both tools issue the same Google search, and google_ai_overview also returns organic results, so there is some overlap. However, google_search is clearly positioned as raw organic output while google_ai_overview targets the synthesized answer, making selection mostly unambiguous.

    Naming Consistency5/5

    Both tools follow the same google_<output> pattern, with consistent snake_case and no stylistic mismatch. Even with only two tools, the naming convention makes each tool's return mode predictable.

    Tool Count3/5

    Two tools is at the thin end of the scale, but the server is tightly focused on Google search output formats. It is borderline: enough to perform one useful task, but there is little room for broader SERP coverage.

    Completeness4/5

    For a server dedicated to Google search results, both the organic listing mode and the AI overview mode are present, so the core search workflow is covered. Missing advanced search types or filters are minor gaps that agents can work around rather than dead ends.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

    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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  ]
}

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How to sync the server with GitHub?

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

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already declare readOnlyHint=true and openWorldHint=true, so the description only needs to add behavioral nuance. It does this by specifying that results are organic, that features like sitelinks and People Also Ask only appear 'when Google renders them,' and that it is appropriate for post-training-cutoff facts, which usefully elaborates the open-world 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?

    Two sentences contain a clear action, a precise list of return content, conditional behavior, and explicit use cases. No words are wasted, and the most important information is front-loaded in the first clause.

    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?

    For a read-only search tool with a fully described schema, the description sufficiently explains what the agent gets and when it is appropriate to use it. There is no output schema, but the returned result types are enumerated; a minor gap is the lack of mention about failures, rate limits, or empty-result behavior.

    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 all seven parameters and their defaults. The description does not add parameter-level detail, which is acceptable under the baseline; the parameter meanings are fully discoverable from the schema.

    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 opens with a specific verb and resource: 'Search Google and get organic results.' It lists concrete returned fields (title, link, snippet, source, position, sitelinks, People Also Ask, related searches), which clearly differentiates it from the sibling google_ai_overview.

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

    Usage Guidelines4/5

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

    The description gives explicit use cases: 'current facts, documentation lookups, and anything past the training cutoff.' It does not name the sibling or provide exclusion criteria, but the use-case framing is clear enough for an agent to judge when to invoke it.

    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?

    Annotations already declare readOnlyHint and openWorldHint, lowering the bar. The description adds useful behavioral context beyond annotations: the response includes a synthesized answer with citations plus organic results, and organic results still come back even when no overview is generated.

    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?

    Three sentences with zero filler. The core value proposition and fallback behavior are front-loaded, and the use-case guidance is compressed into a single clear clause at the end.

    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?

    For a read-only search tool with no output schema, the description covers the main output, the fallback behavior, and the decision rule for selecting it. Parameter semantics are fully handled by the schema, so nothing critical is missing.

    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 baseline is 3. The description does not add parameter-specific detail beyond the schema, but none is strictly needed because every parameter already has a thorough description.

    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?

    States a specific verb and resource: 'Search Google and get its AI Overview' with citations, alongside organic results. The description clearly differentiates this from the sibling by emphasizing the synthesized AI Overview rather than just a list of links.

    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?

    'Use when a summarised answer is more useful than a list of links' gives an explicit when-to-use rule with an implied alternative. It also covers the fallback case where Google renders no AI Overview but organic results still return, making the tool's scope clear.

    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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