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tymrtn

AI or Not MCP Server

by tymrtn
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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 are completely distinct: one performs media analysis and the other checks API liveness. There is no possible confusion between them.

    Naming Consistency4/5

    Both tools share the consistent `aiornot_` prefix and use snake_case, but `analyze_media` is a clear verb_noun while `is_live` is a status-style name rather than an action on an object. Minor deviation in an otherwise predictable pattern.

    Tool Count4/5

    With only two tools, the server is minimal, but the narrow purpose of wrapping the AI or Not API makes the count reasonable. It is slightly thin but not insufficient.

    Completeness5/5

    The analysis tool covers all supported media types (image, video, audio, text) and returns confidence scores, while the liveness tool covers API availability. For the stated purpose, there are no obvious gaps.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns confidence scores, which is helpful, but it does not disclose that this performs network calls to an external API, sends potentially sensitive file content, may have rate limits, or can fail/timeout. Key mutation-like side effects (external data transmission) are absent.

    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 a single sentence with no unnecessary words, and the primary purpose is front-loaded. It loses one point because it is almost too terse, omitting contextual details that could be woven in without adding bulk, such as the need for file_path or text depending on media_type.

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

    Completeness2/5

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

    This is a moderately complex tool with 8 parameters, no output schema, and no annotations. The description explains the basic purpose but not the return format, error behavior, dependency on external API, or the relationship between media_type and required inputs (file_path vs. text). An agent would need to inspect the schema to understand those constraints, but schema does not cover operational or outcome context, leaving a significant gap.

    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 every parameter already has a description. The tool description adds no parameter-level detail beyond what the schema provides. Baseline 3 applies because the schema handles the documentation burden adequately.

    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 'Analyze' and names the exact resource: image, video, audio (music/voice), or text via the AI or Not API. It also states the output (confidence scores), making the tool's function clear and distinguishable from the sibling 'aiornot_is_live'.

    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 about when to use this tool versus alternatives. While it is implied that one would use this for analyzing media, the description does not mention the sibling tool or any non-use cases. The condition 'or text' is the only hint of differentiation, but no exclusions or alternative routing are 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?

    No annotations are provided, so the description carries the full burden. It communicates a simple read-only liveness check, which is fairly transparent for a zero-parameter health probe. However, it does not disclose what the response looks like, error behavior, or whether any state is affected.

    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, clear sentence with no filler or redundant phrasing. Every word contributes to the meaning.

    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 very low complexity, but there is no output schema, so the return value is not explained. The description is otherwise complete for a trivial liveness check, but lacks any usage context or expected-response note.

    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 coverage is effectively 100%. With no parameters to document, the baseline is 4, and the description has no need to add parameter details.

    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 states a specific verb ('check') and resource ('AI or Not API is live'). It clearly distinguishes this health-check tool from the sibling aiornot_analyze_media, which is for media analysis.

    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?

    No guidance is given about when to use this tool versus aiornot_analyze_media, and no preconditions or typical workflows are mentioned. The intended usage is only implied by the tool name and minimal 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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