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Shuxiabit

Mining Rights Daily Agent

by Shuxiabit
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one searches for news articles, the other fetches a specific article's content from a URL. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Both names start with lowercase verbs, but 'search' is a bare verb while 'fetch_article' follows a verb_noun pattern. The inconsistency is minor, but the names are clear and predictable in a small set.

    Tool Count3/5

    With only 2 tools, the set feels thin for a news-focused agent. However, the tools cover the essential search-and-read workflow without unnecessary clutter.

    Completeness4/5

    The pair supports the core flow of searching for mining news and fetching articles. Minor gaps exist, such as no filtering or aggregation, but the basic lifecycle is complete for a read-only news agent.

  • Average 3.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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 present, so the description carries the full burden of behavioral disclosure. It only states the core action and fails to mention important traits such as handling of non-article URLs, paywalls, redirects, or the exact format of the returned text.

    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 a single, front-loaded sentence with no redundant information. Every word contributes to the purpose.

    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?

    The tool is simple with one parameter, but the absence of an output schema and annotations leaves ambiguity about return values and edge-case behavior. The description is adequate for basic use but not fully complete for an agent to anticipate all outcomes.

    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 for 'url' has no description, and the description adds the qualifier that the URL must be public and that the resource is article text. This provides some meaning beyond the raw schema, but it does not detail URL validation, accepted formats, or error scenarios.

    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 verb 'Fetch' and the resource 'readable article text' from a 'public URL', making the tool's purpose specific. It naturally distinguishes itself from the sibling 'search' by focusing on retrieving content from a given URL rather than discovering URLs.

    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 when to use the tool (when you have a public URL and need article text) but does not explicitly mention alternatives or when not to use it. No exclusion criteria or comparison with the sibling 'search' is provided.

    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 of behavioral disclosure. It adds a useful trait: 'Results identify live versus demo data,' which gives insight into output characteristics. However, it does not mention read-only nature, rate limits, or error behavior, so it is adequate but not rich.

    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 short sentences with no redundant information. It front-loads the core purpose and adds a single behavioral detail, making it highly concise and well-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?

    For a tool with two simple parameters and no output schema, the description gives a minimal but functional picture. It covers the search subject and hints at result characteristics (live vs demo), but it does not describe the return structure (e.g., fields like title, date, URL), leaving some uncertainty about the output format.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate. It hints at the 'days' parameter through 'recent' and the 'query' parameter through 'Search,' but it does not clarify how 'days' works or what constitutes a valid query. This leaves parameter semantics vague and largely dependent on inference.

    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 tool's purpose: 'Search recent public mining news.' It uses a specific verb ('Search') and a specific resource ('mining news'), and distinguishes from the sibling tool 'fetch_article' by focusing on discovery rather than retrieval. This makes the tool's role unambiguous.

    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 context ('recent public mining news') but does not explicitly state when to prefer this tool over 'fetch_article' or include exclusions. There is no guidance on alternatives or when not to use it, leaving the agent to infer that search is for finding articles while fetch_article is for retrieving a specific one.

    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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