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spikeon

negative-support-mcp

by spikeon

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.4

  • Disambiguation5/5

    Each tool targets a distinct operation: activation, deactivation, and generation. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    All tool names follow the same pattern: 'negative_support_' prefix plus a clear verb (activate, deactivate, generate). The convention is consistent and predictable.

    Tool Count5/5

    With 3 tools, the set is tightly scoped to the server's purpose of license management and STL generation. Each tool earns its place with no redundancy.

    Completeness5/5

    The core lifecycle is covered: activate a license, deactivate it, and generate output. No obvious missing operations for the stated domain.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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.

  • 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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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

  • Behavior3/5

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

    With no annotations, the description carries the burden of behavioral disclosure. It mentions the token source and process scope, which adds useful context. However, it does not disclose side effects such as whether activation is persistent, overwrites a previous license, or requires a restart. This is a gap for a state-changing operation, but the information provided is at least minimally 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?

    Two sentences, front-loaded with the action. No redundant or filler content. Every sentence adds meaningful information: what the tool does, how to get the token, and an env var alternative. Highly concise and well-structured.

    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 single-parameter tool with an output schema, the description is largely complete. It covers the action, token format, and env var alternative. However, it does not mention whether activation affects other tools or the process lifetime, leaving a minor gap. Given the simplicity of the tool, this is sufficient but not exhaustive.

    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 schema already provides a description for the token parameter (100% coverage). The description adds value by explaining the token format ('ns_live_*'), the source (same as CLI), and an alternative mechanism (env var) that may override or replace the parameter. This goes beyond the schema's simple 'License token' 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 action (activate), the resource (negative.support license), and the scope (for this MCP process). It distinguishes itself from siblings like 'negative_support_deactivate' by indicating the opposite action, and from 'negative_support_generate' by focusing on activation of an existing license.

    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 gives concrete context for use, such as 'Same ns_live_* token as the CLI' and the alternative env var NEGATIVE_SUPPORT_TOKEN. While it doesn't explicitly state when not to use or name alternatives, the sibling tools (deactivate/generate) make the intended use clear. It lacks explicit exclusion criteria but provides practical usage 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?

    With no annotations, the description carries the transparency burden. It discloses the license requirement, long-running behavior, and progress token support to avoid timeouts. It does not detail error conditions or file write side effects, but the core behavioral traits are covered.

    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, front-loaded with the primary purpose and followed by critical usage context. Every sentence adds value with no fluff.

    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?

    The description covers the tool's purpose, license prerequisite, performance expectations, and output types. Given the 8 parameters and the presence of an output schema, it does not need to explain return values. It could mention error handling for missing license, but for a complex tool it is sufficiently complete.

    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 has 100% description coverage for all 8 parameters, so the baseline is 3. The description adds minimal extra semantics beyond the schema, only clarifying that the 3MF includes model and support pieces, which matches the schema descriptions.

    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's action: generate support STL (and optionally 3MF) from local model files. It specifies the input formats and distinguishes itself from the sibling activation/deactivation tools.

    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 notes that an activated license is required, which implicitly tells the agent to use 'negative_support_activate' first. It also provides guidance on heavy meshes and progress tokens, but it does not explicitly mention when not to use the tool or alternatives.

    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 provided, the description carries the full burden for transparency. It discloses the key behavioral limitation (no server-side token revocation) and the local process scope, which is sufficient for this simple, zero-parameter tool. It does not discuss reversibility or permissions, but these are less critical given the tool's simplicity.

    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, well-structured sentence with the main action front-loaded and the critical caveat in parentheses. Every word earns its place, with no 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?

    Given the tool's simplicity (no parameters, output schema exists), the description fully covers what the tool does and its most important non-effect. It is complete enough for an agent to select and invoke the tool correctly.

    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, so the schema coverage is trivially complete. According to the baseline for 0 params, the description does not need to add parameter information. It adds no param semantics but also does not need to.

    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 ('Clear') and resource ('active license') with a scope ('in this process'), and the clarifying parenthetical distinguishes it from server-level revocation. This clearly differentiates it from sibling tools like activate or generate.

    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 context about what the tool does and explicitly notes it does not revoke the token on the server, which helps the agent decide when to use it. However, it does not explicitly name alternative tools or state when not to use it beyond the caveat.

    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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