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greatSumini

Naver Finance Crawl MCP

by greatSumini
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: crawl_stock_detail focuses on retrieving detailed information for a specific stock by its code, while crawl_top_stocks retrieves a list of top searched stocks. There is no overlap or ambiguity between these functions, making it easy for an agent to select the appropriate tool.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with 'crawl_' as the prefix, followed by descriptive nouns (stock_detail and top_stocks). This uniformity enhances readability and predictability, adhering to a clear naming convention throughout the set.

    Tool Count3/5

    With only 2 tools, the server feels thin for a finance crawling domain, as it lacks operations for broader data retrieval, such as market indices, sector analysis, or historical data. While the tools are well-defined, the limited count may restrict agent capabilities in handling more complex financial queries.

    Completeness2/5

    The tool set is significantly incomplete for a finance crawling server. It covers specific stock details and top searches but misses essential operations like crawling market summaries, financial news, historical price data, or sector performance. These gaps will likely cause agent failures when broader financial information is needed.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 is passing

  • 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. While it mentions the tool 'returns comprehensive data,' it lacks behavioral details such as rate limits, authentication requirements, data freshness, error handling, or whether this is a read-only operation. For a data-fetching tool with zero annotation coverage, this is insufficient.

    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 two concise sentences with zero waste. The first sentence states the purpose and parameter, and the second sentence details the return data, making it front-loaded and efficiently structured.

    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 (single parameter, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and return data types but lacks behavioral context and output structure details, which are important for a crawling tool without annotations.

    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 the schema fully documenting the 'stockCode' parameter's type, pattern, and example. The description adds minimal value beyond the schema by mentioning '6-digit code' and implying it identifies a stock, but doesn't provide additional syntax or format details.

    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 ('crawl detailed information'), target resource ('for a specific stock'), and key identifier ('by its 6-digit code'). It distinguishes from the sibling tool 'crawl_top_stocks' by focusing on individual stock details rather than top stocks.

    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 implies usage context by specifying 'for a specific stock by its 6-digit code,' which suggests when to use this tool. However, it doesn't explicitly mention when not to use it or provide alternatives beyond the implied distinction from 'crawl_top_stocks.'

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It discloses the tool's behavior as a data retrieval operation ('Crawl... Returns a list') but lacks details about rate limits, authentication needs, data freshness, or potential side effects. The description doesn't contradict annotations since none exist.

    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 perfectly concise with two sentences: the first states the action and source, the second specifies the return format. Every word adds value with zero wasted text, and information is front-loaded appropriately.

    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 zero-parameter tool with no annotations and no output schema, the description provides adequate context about what data is retrieved and from where. However, it lacks information about output structure details (e.g., list format, data types) that would be helpful given the absence of an output schema.

    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 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics rather than input parameters, establishing a baseline score of 4 for zero-parameter tools.

    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 ('Crawl top searched stocks'), resource ('from Naver Finance'), and output format ('list of the most searched stocks with their codes, names, current prices, and change rates'). It distinguishes from the sibling tool 'crawl_stock_detail' by focusing on aggregated top stocks rather than individual stock details.

    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 implicitly suggests usage when needing aggregated top stock data rather than detailed individual stock information (contrasting with 'crawl_stock_detail'). However, it lacks explicit guidance on when not to use this tool or alternative scenarios beyond 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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