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apiihub33-cyber

ryterpro-mcp

by apiihub33-cyber
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools serve clearly distinct purposes: one performs text humanization, the other provides API configuration and documentation. There is no overlap or ambiguity.

    Naming Consistency4/5

    Mostly consistent with 'humanize_text' following a verb_noun pattern, while 'ryterpro_api_info' is more noun-oriented. The deviation is minor and both names are clear.

    Tool Count3/5

    With only 2 tools, the server feels thin for a text-processing API, but the scope is narrow enough that the count is acceptable.

    Completeness4/5

    The core functionality of humanizing text is covered, and the info tool provides necessary context. Minor gaps like batch processing or configuration options are not essential for the stated purpose.

  • Average 2.7/5 across 2 of 2 tools scored. Lowest: 1.5/5.

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

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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

  • Behavior1/5

    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 gives no information about side effects, authentication requirements, rate limits, or what happens to the input text. The phrase 'Humanize text' is completely opaque regarding API behavior.

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

    Conciseness2/5

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

    Although the description is a single sentence, it provides no substantive value beyond restating the tool name. This is under-specification rather than conciseness, as no information is front-loaded to aid the agent.

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

    Completeness1/5

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

    The tool has two parameters, no schema descriptions, and no annotations, yet the description fails to address what humanization does, what 'mode' controls, or any usage conditions. Even with an output schema present, the description is grossly incomplete for correct invocation.

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

    Parameters1/5

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

    The schema has 0% description coverage, and the description does not explain the 'text' parameter beyond the tool name, while completely ignoring the optional 'mode' parameter. This leaves the agent without essential parameter semantics, especially for 'mode' which has no definition.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Humanize text using the Ryter Pro API' essentially restates the tool name without explaining what 'humanize' means or what the transformation entails. It does not distinguish the tool from its sibling ryterpro_api_info, providing no unique functional context.

    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 guidance on when to use this tool versus the sibling tool or any other alternatives. The description lacks any indication of typical use cases, prerequisites, or contexts where this tool is appropriate.

    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. The verb 'Return' suggests a read-only, non-mutating operation, and the tool appears to be a simple info endpoint. However, it does not disclose potential side effects, authentication requirements, or any limitations, leaving gaps for an agent to infer.

    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 that front-loads the action ('Return') and the object ('Ryter Pro MCP server configuration and documentation links'). Every word earns its place, with no filler or repetition.

    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 the tool's simplicity—no parameters and an output schema that presumably documents return values—the description is complete enough. It describes the tool's purpose sufficiently without needing to explain configuration details or documentation URLs.

    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 the schema confirms this with an empty properties object. The baseline for no parameters is 4, and the description does not need to add parameter-level detail since there are none.

    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 the specific verb 'Return' and identifies a clear resource: 'Ryter Pro MCP server configuration and documentation links.' It clearly distinguishes this tool from the sibling 'humanize_text' by stating a completely different function.

    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, no mention of when not to use it, and no reference to sibling tools. Usage is only implied by the tool's name and generic description, which is insufficient for clear decision-making.

    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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