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ktozvonil

mcp-server-ktozvonil

by ktozvonil
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one for querying phone number information, the other for posting a review. No overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: 'lookup_phone' and 'post_comment'. Names are clear and predictable.

    Tool Count3/5

    With only 2 tools, the server feels minimal but arguably covers the core use case (lookup and contribute). However, it is on the low end of the range.

    Completeness3/5

    The server provides read and write capabilities for phone number reviews, but lacks update/delete or moderation features. Basic workflow is covered, but there are notable gaps for a full review lifecycle.

  • Average 4.4/5 across 2 of 2 tools scored.

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

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

  • 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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    {
  "$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 are provided, so the description carries the full burden. It clearly identifies the operation as a lookup and discloses the output fields (community verdict, rating, spam level, operator, region, tags, recent reviews). There is no mention of rate limits, auth, or edge cases, but for a straightforward read-only lookup, the provided detail 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 sentence that starts with the main action 'Look up' and efficiently packs all essential information—website, output categories—without redundancy or filler.

    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?

    The tool is simple: one parameter with full schema coverage, no output schema required, and no complex behavior. The description fully covers what the tool does and what it returns, making it complete for an agent to select and invoke it correctly.

    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% for the 'number' parameter, so the baseline is 3. The description does not add parameter-specific details beyond the schema, but none are needed given the schema already explains the format and meaning.

    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 uses a specific verb 'Look up' with a clear resource 'phone number on ktozvonil.net' and enumerates the returned data (community verdict, rating, spam level, operator, region, tags, recent reviews). This clearly distinguishes it from the sibling tool 'post_comment', which serves a different purpose.

    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 when to use the tool: whenever you need phone number information from ktozvonil.net. The only sibling tool, 'post_comment', has an obviously distinct purpose, so the usage context is clear even though no explicit exclusions or alternative recommendations are stated.

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

  • Behavior5/5

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

    With no annotations, the description carries full burden and discloses critical behavior: the review is labelled AI-agent-generated, shown in a separate section, and does NOT affect the community rating or verdict. This goes beyond basic write semantics and helps the agent understand side effects.

    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?

    Three tightly focused sentences: purpose, authentication, and side-effect disclosure. Each sentence carries essential information with no redundancy or filler.

    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 write tool with no output schema or annotations, the description covers purpose, auth requirements, and behavioral impact. It lacks mention of return values or error scenarios, but these are not essential given the tool's simplicity.

    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?

    Input schema covers 100% of parameters with meaningful descriptions (text, number, rating). The tool description adds no additional parameter context, so it meets the baseline without enhancing beyond schema.

    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 clearly states the action ('Post a phone-number review') and the target resource, distinguishing it from the sibling lookup_phone. It also specifies 'on behalf of the authenticated user,' which adds precise context.

    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?

    Provides clear context for use (requires OAuth write scope, discoverable via /.well-known/oauth-protected-resource), and the sibling 'lookup_phone' makes the alternative obvious. However, it does not explicitly state when not to use this tool or contrast with lookup_phone.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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