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alexcong

Gemini DeepSearch MCP

by alexcong
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'deep_search' has a clear, distinct purpose focused on performing deep web research.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'deep_search' follows a clear verb_noun pattern and is descriptive of its function.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and flexibility. While it might suffice for a very narrow scope, it feels thin and incomplete for a server named 'Gemini DeepSearch MCP', which suggests broader research capabilities.

    Completeness2/5

    The server is severely incomplete for a deep search/research domain. There are no tools for refining searches, managing search history, filtering results, or handling different types of queries. The single tool provides a basic search function but lacks the comprehensive coverage expected for a research-oriented server.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • 0 of 2 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • 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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'advanced web research agent' and returns a file path to JSON, but doesn't cover critical aspects like rate limits, authentication needs, execution time, or what 'deep' entails operationally. For a tool with no annotation coverage, this leaves significant behavioral traits undisclosed.

    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 sized and front-loaded with the core purpose. The 'Args' and 'Returns' sections are structured but slightly verbose (e.g., 'low, medium or hight' has a typo). Overall, it's efficient with minimal waste, though not perfectly polished.

    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 complexity (web research with effort levels), no annotations, and an output schema present (implied by 'Returns' statement), the description is moderately complete. It covers the basic operation and return format but lacks details on behavioral traits and usage context, making it adequate but with clear gaps.

    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 description adds minimal semantics beyond the input schema. It explains 'query' as 'the research question or topic to investigate' and 'effort' as 'the amount of effect for the research', but the schema already has 100% coverage with clear descriptions. The description's param info is redundant, so it meets the baseline of 3 without adding significant value.

    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: 'Perform a deep search on a given query using an advanced web research agent.' It specifies the verb ('perform a deep search') and resource ('web research'), though it doesn't differentiate from siblings since none exist. The purpose is specific and actionable, not vague or tautological.

    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 mentions 'advanced web research agent' but doesn't explain what makes it 'deep' or in what contexts it's preferred over other search methods. With no sibling tools, the lack of explicit usage context is a notable gap.

    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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