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ekaone

ekaone/mcp-tools

by ekaone
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

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

  • Latest release: v0.0.5

  • Disambiguation5/5

    The two tools are clearly distinct: one masks credit/debit card numbers and the other masks email addresses. There is no overlap in their purposes.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern: mask_card and mask_email. The naming is consistent and predictable.

    Tool Count3/5

    With only two tools, the server feels somewhat thin. The count is borderline for the apparent purpose of data masking, though not excessively small.

    Completeness2/5

    The domain appears to be masking sensitive data, but only card and email are covered. Common types like phone numbers, SSNs, or IP addresses are missing, leaving notable gaps.

  • Average 4.2/5 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
    • Last stable release on
    • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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.

  • Add related servers to improve discoverability.

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?

    With no annotations provided, the description carries the burden of behavioral disclosure. It reveals PCI DSS compliance by default (last 4 digits only) and lists supported card types. However, it does not describe return behavior or error handling, leaving some ambiguity.

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

    Conciseness4/5

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

    The description is four sentences long, with each sentence contributing distinct information: purpose, usage, supported types, and compliance. It is efficient and front-loaded with the primary action.

    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 simplicity and full schema coverage, the description covers purpose, usage, and key behavioral defaults. It does not explicitly describe the return value, but for a masking operation this is largely implied. Overall, it is sufficiently complete for an agent to use 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?

    The input schema provides 100% description coverage for all four parameters, so the description adds little beyond what the schema already states. The schema itself explains the value parameter accepts formatted input, so there is no additional semantic value from the description.

    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 masks credit/debit card numbers to protect sensitive payment information, using the specific verb 'masks' and resource. It distinguishes from sibling 'mask_email' by focusing on card types. The purpose is unambiguous.

    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 provides clear usage context: 'Use when the user wants to hide, protect, or anonymize card numbers in a table, display, or logs.' However, it does not explicitly mention alternatives or exclusions, so it stops short of full guidance.

    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?

    No annotations are provided, so the description carries the burden. It discloses default behavior: 'By default shows the first 2 characters of the username and keeps the domain visible.' This adds context not available in the schema, such as the exact masking behavior. It could be richer by describing output format or edge cases, but for a simple pure function it 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 two sentences long, the first stating the core purpose and the second conveying usage context and default behavior. Every word serves a purpose, with no redundancy or unrelated information. It is front-loaded with the action.

    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 that the tool is a simple string transformation with no output schema and no annotations, the description adequately covers purpose, usage, and defaults. It does not explicitly describe the return value or behavior for invalid input, but these are not critical for a straightforward masking function. The overall definition is sufficiently complete for an agent to 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%, so all four parameters are documented with defaults and examples. The description's statement about default behavior (first 2 characters, domain visible) essentially re-states the schema defaults for visibleChars and maskDomain, adding no new parameter-level meaning. Hence, baseline is 3 with no additional value.

    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 begins with a specific verb and resource: 'Masks an email address to protect user privacy.' This clearly distinguishes it from the sibling tool mask_card by specifying the resource type (email address) and the action (masking).

    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 provides clear usage context: 'Use when the user wants to hide, anonymize, or partially obscure email addresses in a table, UI, or logs.' This tells the agent when to apply the tool, though it does not explicitly mention alternatives or exclusions. The sibling mask_card inferentially covers cards, so the usage boundary is clear.

    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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Glama performs regular codebase and documentation scans to:

  • 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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