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tanamurayuuki

Gemini URL Context & Search MCP Server

by tanamurayuuki
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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: one performs web searches, while the other extracts content from specific URLs. There is no overlap in functionality, making it easy for an agent to select the correct tool based on whether it needs general search results or URL-specific content extraction.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: 'google_search' and 'url_context_extract'. The naming is clear, descriptive, and adheres to a uniform style, making the tools easily identifiable and predictable in their naming convention.

    Tool Count2/5

    With only two tools, the server feels thin for its purpose of 'Gemini URL Context & Search'. While the tools cover search and extraction, the scope suggests potential for more operations, such as managing search history or handling multiple URLs, making the tool count insufficient for comprehensive coverage.

    Completeness3/5

    The tools cover basic search and URL content extraction, but there are notable gaps. For example, there's no tool for saving or managing extracted data, refining searches, or handling batch URL processing. This limits the server's ability to support more advanced workflows within its domain.

  • Average 3.2/5 across 2 of 2 tools scored.

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

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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 using Gemini AI and returning structured JSON, but lacks details on rate limits, authentication needs, error handling, or what 'extract content' entails (e.g., web scraping, API calls). For a 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 a single, efficient sentence that front-loads the core purpose. It avoids unnecessary words and directly communicates the tool's function. However, it could be slightly more structured by separating key components (e.g., input, process, output) for clarity.

    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 complexity (AI-powered extraction with 4 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the output structure (what 'pages, answer, and metadata' contain), error cases, or behavioral traits like rate limits. For a tool with no structured support, the description should provide more context to be fully helpful.

    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%, so the schema already documents all four parameters (urls, query, model, maxCharsPerPage) with descriptions. The tool description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

    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: 'Extract content from URLs using Gemini AI and return structured JSON with pages, answer, and metadata.' It specifies the verb (extract), resource (content from URLs), technology used (Gemini AI), and output format (structured JSON). However, it doesn't explicitly differentiate from the sibling tool 'google_search', which likely serves a different purpose (searching vs. extracting).

    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 tools, nor does it specify prerequisites, exclusions, or typical use cases. The agent must infer usage from the purpose alone.

    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. It mentions that the tool 'Provides search results with sources and citations,' which gives some behavioral context, but does not disclose critical traits such as rate limits, authentication needs, error handling, or pagination. For a web search tool with no annotations, this leaves significant gaps in understanding its operational 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?

    The description is front-loaded and efficiently structured in two sentences: the first states the tool's purpose and method, and the second specifies the output. Every sentence adds value without redundancy, making it appropriately sized and easy to parse.

    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 search with 3 parameters), no annotations, and no output schema, the description is partially complete. It covers the basic purpose and output format but lacks details on behavioral traits, error handling, and usage guidelines. This is adequate for a simple tool but has clear gaps in providing a full operational understanding.

    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 parameters (query, instruction, model) with descriptions. The description adds minimal value beyond the schema, as it does not provide additional syntax, format details, or usage examples for the parameters. Baseline 3 is appropriate when the schema handles most of the parameter documentation.

    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 specific action ('Search the web') using a specific resource ('Google Search grounding via Gemini API') and distinguishes from the sibling tool 'url_context_extract' by focusing on web search rather than URL extraction. It specifies what the tool provides ('search results with sources and citations').

    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 implies usage for web search tasks but does not explicitly state when to use this tool versus the sibling 'url_context_extract' or other alternatives. It provides basic context (searching the web) but lacks explicit guidance on exclusions or specific scenarios where this tool is preferred.

    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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