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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one checks a single IP address, the other checks multiple IPs in bulk. An agent can easily distinguish between them based on the singular/plural naming and the explicit descriptions.

    Naming Consistency5/5

    Both tools follow the consistent pattern of 'check_' followed by a noun indicating the input type ('ip' vs 'ips'). The naming is predictable and uses consistent snake_case throughout.

    Tool Count3/5

    With only two tools, the server feels minimal for its domain. While both tools are necessary and well-scoped, a more comprehensive IP intelligence server would typically benefit from additional tools (e.g., contextual enrichment, history). The count is borderline thin.

    Completeness3/5

    The tool set covers the basic use case of checking IPs against GreyNoise data, both individually and in bulk. However, it lacks other common operations like fetching detailed threat context, searching by tags or queries, or retrieving known-good RIOT entries specifically. Notable gaps exist for a more complete threat intelligence surface.

  • Average 3.7/5 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 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

  • Behavior3/5

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

    No annotations are provided, so the description bears the full burden. It explains the two classification outcomes (noise vs. targeted, and RIOT good IPs) but does not specify the actual output format, fields, or error behavior. The conceptual outline is helpful but lacks concrete details.

    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 concise with four sentences, front-loading the core purpose. Every sentence adds value: it defines the action, explains the background, and clarifies interpretation. No redundant or irrelevant content.

    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?

    For a simple lookup tool, the description covers the reasoning behind the classification, but it lacks explicit mention of the return format or fields. The existence of a sibling ('check_ips') suggests batch capability is not clarified, which could confuse an agent. The description is adequate but not fully 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?

    There is one parameter with 100% schema description coverage (example provided). The tool description adds conceptual context about what the IP is checked against, but does not add new constraints or semantic details beyond the schema. Baseline 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 tool checks if an IP is internet background noise or potentially targeted activity, using GreyNoise and RIOT datasets. It provides a specific verb and resource. However, it does not explicitly differentiate from the sibling tool 'check_ips', which likely handles multiple IPs.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explains the context (security analysis, noise vs. targeted) but does not provide explicit guidance on when to use this tool over 'check_ips' or any exclusion criteria. The use case is implied but not directly stated.

    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, the description carries the full burden. It discloses rate limits (50/day for free tier) and the batch nature, but does not mention idempotency, error behavior, or whether the tool is read-only. This leaves gaps in the agent's understanding of side effects.

    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, each earning its place: the first states the action and target, the second provides a use case and a critical constraint (rate limits). No redundancy or excess.

    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 has 1 parameter with full schema coverage, but no output schema. The description does not explain what the tool returns (e.g., risk scores, noise classification), which is essential for an agent to interpret results. It also lacks authentication requirements or explicit differentiation from 'check_ip', leaving the agent slightly under-informed.

    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 the baseline is 3. The description adds no new information about the 'ips' parameter beyond what the schema already provides (array of strings, max 10, min 1). It does not clarify IP format or validation rules.

    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 verb 'Check' and the resource 'multiple IP addresses against GreyNoise'. It also distinguishes itself from the sibling tool 'check_ip' by emphasizing batch capability ('in one call'), leaving no ambiguity about its purpose.

    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: 'useful for triaging a list of suspicious IPs from logs or alerts.' It also mentions rate limits. While it does not explicitly contrast with 'check_ip' or state when not to use it, the use case is well-defined and implicitly differentiates from a single-IP check.

    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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