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yoshiko-pg

o3-search MCP

by yoshiko-pg
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between tools.

    Naming Consistency5/5

    With a single tool, naming is inherently consistent; the name 'o3-search' clearly indicates its search function.

    Tool Count3/5

    Having only one tool feels thin for a search server, but the tool is non-trivial and covers general web search, so it's borderline acceptable.

    Completeness4/5

    The single tool covers web search comprehensively with natural language support, though more specialized search options could be beneficial.

  • Average 4/5 across 1 of 1 tools scored.

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

    • No community issues 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

  • Behavior3/5

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

    With no annotations provided, the description bears full burden. It mentions 'advanced web search capabilities' and 'supports natural language queries', but it does not disclose limitations, return format, or error handling. This is adequate but not rich in behavioral context.

    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 with no filler. The first sentence states the primary function, and the second provides usage context. Every sentence is valuable and front-loaded.

    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 simple tool with one parameter and no output schema, the description covers purpose, use cases, and input language specification. It could mention output format or limitations, but it is sufficiently complete for an LLM to understand the tool's role.

    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% with one parameter. The description adds that queries should be in English and can include complex problems, which slightly enhances the schema's param description. However, since coverage is high, the baseline is 3.

    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 'AI agent with advanced web search capabilities', providing a specific verb (search) and resource (web). It also lists use cases like finding latest information, troubleshooting errors, and discussing ideas, which clarifies its purpose without needing sibling differentiation.

    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 explicitly says 'useful for finding the latest information, troubleshooting errors, and discussing ideas or design challenges', giving clear context on when to use the tool. No siblings exist, so no exclusion guidance is needed.

    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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