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mkih76

gemini-search-mcp

by mkih76
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.4.0

  • Disambiguation2/5

    The tools 'ask' and 'web_search' have nearly identical descriptions, both using Google AI Mode and returning AI-generated answers. The only distinction is that 'ask' may optionally skip web search, but this is unclear and overlapping, making it hard for an agent to choose reliably.

    Naming Consistency2/5

    Tool names 'ask' (verb) and 'web_search' (noun with underscore) do not follow a consistent pattern. Mixing a simple verb with a compound noun creates inconsistency, and no clear naming convention is evident across the set.

    Tool Count3/5

    With only 2 tools, the server feels thin for a search service, but it is scoped to Google AI Mode. The count is borderline; it lacks additional tools for raw results or specialized searches, but the core functionality is covered.

    Completeness4/5

    For the narrow purpose of AI-powered search with synthesis, the server covers asking general questions and web searches. Minor gaps exist (e.g., no way to get unsynthesized results), but overall the surface is reasonably complete for its stated domain.

  • 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
    • 7 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

  • Behavior4/5

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

    Annotations (readOnlyHint=true) indicate read-only operation, which matches the description. Description adds detail about real-time search, synthesis, and grounding in web content, going beyond 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?

    Concise and well-structured: front-loaded with purpose, then details, equivalence explanation, and structured 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 the tool's simplicity and presence of an output schema, the description covers purpose, usage, and behavior well. Minor gap: no mention of potential limitations or failure modes.

    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?

    Schema description coverage is 0%, so description must compensate. It provides comprehensive semantics for the 'query' parameter: describes it as a search query or question, gives examples, and clarifies it can be anything typed into Google.

    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?

    Description clearly states the verb ('search'), resource ('web'), and mode ('Google AI Mode') with a synthesized answer. It distinguishes from the sibling tool 'ask' by specifying it uses Google's AI Mode, which is a specific search mode.

    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?

    Provides examples of queries and states it is equivalent to Google's AI Mode tab, but does not explicitly say when not to use it or compare to alternatives like 'ask'. No exclusion criteria or alternative guidance.

    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 indicate readOnlyHint=true. The description adds that AI Mode may search the web or answer from training data, and that the answer is 'potentially grounded in web search results', which provides useful behavioral context beyond the annotation.

    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 concise with two short paragraphs and a clear argument description. It front-loads the purpose and avoids unnecessary details, making it easy for an agent to parse.

    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 the tool's simplicity (one parameter, output schema exists), the description adequately covers purpose, usage, behavior, and return value. It lacks error scenarios or edge cases, but for a straightforward tool, it is complete enough.

    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% description coverage for the single parameter 'prompt'. The description compensates by describing it as 'Any question or instruction' and explaining how it is processed (web search if needed). This provides sufficient semantic meaning beyond 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 clearly states 'Ask Google AI Mode any question and get an AI-generated answer', directly indicating the verb (ask) and resource (Google AI Mode). It explicitly contrasts with sibling tool 'web_search', clarifying its purpose for general questions.

    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?

    The description explicitly states it is 'Similar to web_search but intended for general questions that may or may not require web search', providing clear when-to-use guidance and differentiation from the sibling tool.

    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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  • Evaluate tool definition quality.

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