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captainChaozi

Search Intent MCP

by captainChaozi
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JavaScript
Remote

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 ambiguity or overlap between tools. The tool has a clear, singular purpose focused on search intent analysis, making it impossible for an agent to misselect between non-existent alternatives.

    Naming Consistency5/5

    The single tool name follows a consistent snake_case pattern (search_intent_analysis). Since there is only one tool, naming consistency is inherently perfect with no deviations or mixed conventions to evaluate.

    Tool Count2/5

    A single tool is too few for a server named 'Search Intent MCP', which suggests a broader scope for search-related operations. While the tool covers analysis well, the set lacks complementary tools for actions like refining searches, tracking trends, or managing intents, making it feel thin and incomplete for the domain.

    Completeness2/5

    The tool surface is severely incomplete for search intent analysis. It only provides analysis without supporting operations like saving intents, comparing queries over time, or integrating with search engines. This creates significant gaps that will limit agent workflows, as analysis alone is a dead end without follow-up actions.

  • Average 2.5/5 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
    • 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 ISC 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 full burden. It mentions features like analyzing intent and providing suggestions, but doesn't disclose behavioral traits such as rate limits, authentication needs, response time, or error handling. The examples and response format hint at output structure, but lack operational context.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is structured with sections like Features, Examples, and Response format, but includes redundant information (e.g., listing features that are somewhat repetitive). It could be more front-loaded; the core purpose is stated upfront, but subsequent details could be streamlined to avoid duplication.

    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 no annotations, no output schema, and a single parameter with full schema coverage, the description is incomplete. It lacks details on behavioral aspects, error cases, and practical usage scenarios. The response format section partially compensates, but overall, it doesn't provide enough context for reliable tool invocation.

    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 'query' documented as 'Enter a search term to analyze'. The description adds no additional meaning beyond this, as it doesn't elaborate on parameter constraints, formats, or examples. Baseline score of 3 is appropriate since the schema adequately covers the parameter.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool analyzes search intent and user behavior, which provides a general purpose. However, it lacks specificity about what resources it operates on (e.g., search queries, logs) and doesn't differentiate from siblings (though none exist). The phrase 'analyzing search intent' is somewhat vague without concrete examples of what analysis entails.

    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?

    No explicit guidance is provided on when to use this tool versus alternatives. The description lists features and examples, but these don't constitute usage instructions. Without sibling tools, there's no need for differentiation, but it still fails to specify prerequisites, constraints, or ideal scenarios for invocation.

    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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