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Spritualkb

nuclei-server MCP Server

by Spritualkb
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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one starts a scan and the other cancels it. There is no overlap or ambiguity between these operations, making it easy for an agent to select the correct tool for the intended action.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (start_scan, cancel_scan), using snake_case throughout. This predictable naming scheme enhances readability and reduces confusion for agents.

    Tool Count2/5

    With only 2 tools, the server feels too thin for a scanning domain, as it lacks essential operations like retrieving scan results, listing scans, or configuring scans. This minimal set may force agents into dead ends or require workarounds.

    Completeness2/5

    The tool surface is severely incomplete for a nuclei scanning server. While start and cancel are basic actions, there are significant gaps: no way to get scan status, view results, list scans, or manage templates. This will likely cause agent failures in typical scanning workflows.

  • Average 2.9/5 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

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  • 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 full burden for behavioral disclosure. 'Cancel' implies a mutation operation that stops an ongoing process, but the description doesn't address important behavioral aspects: whether cancellation is reversible, what permissions are required, what happens to partial scan results, or how to verify the scan was running. For a mutation tool with zero annotation coverage, this is inadequate.

    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 maximally concise - a single four-word sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple tool with one parameter and clear basic functionality.

    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?

    For a mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address the tool's behavioral implications, error conditions, or relationship to the sibling 'start_scan' tool. The agent would need to guess about important aspects like what constitutes a 'running' scan, cancellation effects, and verification of success.

    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 schema has 100% description coverage, with the single parameter 'scanId' clearly documented in the schema. The description doesn't add any parameter semantics beyond what's already in the schema, but since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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

    Purpose4/5

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

    The description clearly states the action ('cancel') and target ('a running scan'), providing specific verb+resource. However, it doesn't differentiate from the sibling tool 'start_scan' beyond the obvious verb difference, missing an opportunity to clarify the relationship between these complementary operations.

    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?

    The description provides no guidance on when to use this tool versus alternatives, prerequisites, or constraints. While the presence of 'start_scan' as a sibling suggests a workflow relationship, the description doesn't explicitly state this or provide any usage context beyond the basic action.

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

  • 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 states the tool starts a scan but fails to describe what happens during execution (e.g., whether it runs asynchronously, potential impacts on targets, or expected outputs). This leaves critical behavioral traits undocumented for a tool with security implications.

    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 extremely concise with a single, front-loaded sentence ('Start a new nuclei scan') that directly conveys the core purpose without any wasted words. This efficiency makes it easy to parse, though it may lack depth.

    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?

    Given the tool's complexity (9 parameters, no annotations, no output schema, and security-related functionality), the description is insufficient. It doesn't cover behavioral aspects, output expectations, or usage context, leaving significant gaps for an AI agent to understand how to invoke it correctly and interpret results.

    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 schema description coverage is 78%, which is relatively high, setting a baseline of 3. The description adds no additional parameter information beyond what the schema provides, such as explaining the relationship between parameters or typical values. It doesn't compensate for the 22% gap in coverage, but the schema handles most documentation.

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

    Purpose4/5

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

    The description clearly states the action ('Start a new nuclei scan') with a specific verb ('Start') and resource ('nuclei scan'), making the purpose immediately understandable. However, it doesn't distinguish this from its sibling tool 'cancel_scan' or explain what a 'nuclei scan' entails, which prevents a perfect score.

    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?

    The description provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or context for initiating a scan. While it implies usage for starting scans, there's no explicit advice on timing, constraints, or how it relates to 'cancel_scan'.

    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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