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Sophomoresty

gemini-search-mcp

by Sophomoresty
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.4.0

  • Disambiguation4/5

    The two tools have distinct intentions: 'ask' for general questions that may use training data, and 'web_search' for explicit web-grounded answers. The description of 'ask' clarifies it is similar but flexible, reducing ambiguity. However, an agent might still be uncertain which to choose when a question could be answered from either source.

    Naming Consistency3/5

    'ask' is a single verb, while 'web_search' is verb_noun with underscore. They do not follow a consistent pattern, which could be slightly confusing. However, both names are descriptive and readable.

    Tool Count3/5

    With only 2 tools, the server feels thin for a search/QA domain. Typically a well-scoped server has 3-15 tools. However, for a specialized server focused on Google AI Mode, two tools (one for general questions and one for web search) might suffice, but it's on the lower end of acceptable.

    Completeness3/5

    The server covers two modes of AI-powered answering, but lacks a tool for pure training-data answers without web search, as 'ask' may still use web search. Users cannot force offline-only answers. Additionally, there is no tool for raw search results, only synthesized answers. This leaves notable gaps for a comprehensive search toolset.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 21 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.

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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

  • Behavior4/5

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

    The description adds context beyond the readOnlyHint annotation by explaining it searches in real-time, synthesizes answers from multiple sources, and is powered by Gemini. It is consistent with annotations.

    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 well-structured with a clear first-line purpose, followed by technical context, and separate Args/Returns sections. Every sentence adds value.

    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?

    Given a single required parameter and an output schema, the description adequately covers behavior and return format. Minor omission: no mention of limitations or rate limits.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Despite 0% schema description coverage, the description provides extensive guidance on the 'query' parameter with examples, clarifying it can be any search query or question.

    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 tool searches the web using Google AI Mode and returns a synthesized answer with sources. It is distinct from sibling 'ask' tool.

    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 gives context that this is for general web queries and provides examples, but does not explicitly state when not to use it or compare it to alternatives like 'ask'.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

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

    Describes the automatic decision process between web search and training data, and specifies return type as 'AI-generated answer, potentially grounded in web search results'. Annotations already declare readOnlyHint=true, and description aligns without contradiction.

    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?

    Concise with no fluff. Front-loaded with purpose, then comparison, then structured Args and Returns sections. Every sentence adds value.

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

    Completeness5/5

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

    With only one parameter and an output schema (implied), the description covers the tool's behavior, input, and output adequately. Mentions sibling tool for differentiation.

    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?

    Schema has 0% description coverage for the parameter, but the description provides an explicit Args section explaining 'prompt: Any question or instruction. Google AI Mode will search the web if needed and synthesize an answer.' This adds meaningful semantics beyond the schema type.

    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?

    Clearly states 'Ask Google AI Mode any question and get an AI-generated answer' - specific verb+resource. Distinguishes from sibling web_search by noting it's for general questions that may not require web search.

    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?

    Explicitly compares to web_search and explains that Google AI Mode decides whether to search the web or answer from training data, providing good context on when to use. Lacks explicit 'when not to use' but covers key distinction.

    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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