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DarynaFor

varta-mcp

by DarynaFor
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: check_spam_text classifies a single message, get_spam_stats provides aggregate counts, and get_spam_examples shows recent samples. There is no overlap in functionality or confusion about which tool to use for a given task.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: check_spam_text, get_spam_stats, get_spam_examples. The verbs (check, get) and nouns (text, stats, examples) are clear and predictable.

    Tool Count4/5

    With three tools, the server is on the lower end of the ideal range, but it is well-scoped for a narrow spam-classification utility. Each tool serves a distinct need, and the count feels appropriate rather than sparse.

    Completeness4/5

    The core functionality (classifying a text) is covered, along with supporting statistical and example retrieval. Minor gaps exist (e.g., batch processing or category filtering), but these are not essential for the stated purpose and can be worked around.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 3 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 MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior4/5

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

    No annotations exist, so the description carries the burden. It provides useful behavioral context: returns exactly 5 most recent public examples and includes verdict, category, and a link to full analysis. It does not discuss auth, rate limits, or pagination, but for a zero-parameter read tool this is adequate.

    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 concise sentence, front-loaded with the action verb 'Get', and contains no filler or redundant information.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given 0 parameters and no output schema, the description is complete for its scope. It communicates the count (5), scope (most recent public), subject (spam/scam examples), and return content (verdict, category, link).

    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 tool has no parameters, so the schema defines nothing. The baseline for zero parameters is 4, and the description adds value by describing the output fields, which is more than enough for a parameterless tool.

    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 retrieves the 5 most recent public spam/scam examples from the isitaspam.com classifier. This distinguishes it from sibling tools like check_spam_text (checks a text) and get_spam_stats (gets statistics) by focusing on example records.

    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?

    There is no explicit guidance on when to use this tool versus alternatives. The description implies it is for fetching example records, but it does not mention check_spam_text or get_spam_stats, or specify any prerequisites or exclusions.

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

  • Behavior4/5

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

    With no annotations, the description carries the full burden. It discloses the use of GPT, Claude, and Gemini, vector similarity, and the output fields (verdict, confidence, category, risk signals, red flags, recommendation). Missing are potential limitations or failure modes, but the core behavior is well described.

    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 three short sentences, each earning its place: the classification action, the return values, and the data provenance. There is no unnecessary detail or repetition.

    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?

    Given the tool's moderate complexity and the absence of an output schema, the description compensates by listing the return fields. It covers purpose, method, and outputs but omits edge cases like error handling or accuracy caveats, which are minor for this use case.

    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 input schema already provides thorough descriptions for both 'text' (max 5000 chars) and 'locale' (ISO 639-1, auto-detected). The tool description adds no extra parameter-specific meaning, so the schema does the heavy lifting, warranting the baseline score.

    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 starts with 'Classify a text message as SPAM, SUSPICIOUS, or SAFE' – a specific verb, resource, and outcome. The mention of multi-LLM consensus and vector similarity against scam patterns further differentiates it from sibling tools like get_spam_stats and get_spam_examples.

    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 clearly implies the tool is for classifying text messages, distinguishing it from sibling tools that provide stats or examples. However, it does not explicitly state when not to use this tool or directly reference alternatives, so it falls short of a 5.

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

  • Behavior4/5

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

    With no annotations, the description discloses a key behavioral trait: 'Cached 60 seconds,' indicating potential staleness. It also enumerates exactly which statistics are returned (total checks, SPAM, SUSPICIOUS), providing useful context beyond what an empty schema would imply.

    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 immediate verb-first structure. The source, key metrics, and caching behavior are all stated without any filler or redundancy.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    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 output schema, the description covers the data source, exact statistics, time scope ('today'), and cache lifetime. This is fully sufficient for an agent to know what to expect.

    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 tool has zero parameters and 100% schema coverage (trivial). Baseline 4 applies because the description need not explain parameters that don't exist.

    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?

    Description opens with 'Get daily spam classification statistics' – a specific verb and resource. It distinguishes itself from siblings (check_spam_text, get_spam_examples) by focusing on aggregate metrics rather than text analysis or example retrieval.

    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 context implies use when one needs general spam stats, but it does not explicitly state when to use it over alternatives or when not to use it. Sibling tools are listed separately but not referenced in the description.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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