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Baneado98

ip-doctor

by Baneado98
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are clearly distinct: check_ip handles single targets while check_many handles batch processing. Descriptions explicitly differentiate the use cases.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: 'check' + target type (ip or many). Simple and predictable.

    Tool Count3/5

    Only 2 tools for IP/domain reputation checking feels thin. While the core functionality is covered, the server would benefit from additional tools for advanced features like whitelist management or historical queries.

    Completeness4/5

    The tool set covers the primary use case of live reputation checking for single and multiple targets. Minor gaps exist (e.g., no tool for checking URLs or managing blocklists), but the domain is simple and adequately served.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 5 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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 reveals that checks are 'live' and returns 'concise verdict per target'. It does not disclose authorization needs, rate limits, or whether it is read-only (but reasonable to infer for a check). Could be more thorough but 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, no filler, front-loaded with the core action ('run the live IP/domain reputation check'). Every sentence adds value.

    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 description explains the return format ('one concise verdict per target') but lacks details on verdict structure, error handling, or behavior with invalid targets. Without an output schema, more could be said, but it covers the essential outcome.

    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 coverage is 100% for the single parameter 'targets', which already documents its type and meaning. The description adds example usage ('from your logs') but no additional syntax or constraints, so it meets baseline 3.

    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 runs live reputation checks on multiple IPs/domains at once and returns a verdict per target. It distinguishes from the sibling check_ip by explicitly indicating batch operation.

    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 usage context ('batch-vetting traffic', 'blocklist candidate set') and implies when to use this over check_ip (for multiple targets). However, it does not explicitly state when not to use it or mention alternative tools.

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

  • Behavior5/5

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

    No annotations are provided, so the description carries full responsibility for transparency. It thoroughly explains the tool's behavior: real-time DNS lookups, DNSBL checks, Tor-exit status, abuse score calculation, and the effect of the deep parameter. It also notes that domains are resolved to their A record.

    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 paragraph but is well-organized and front-loaded with the main purpose. It uses clear language and avoids redundancy. Breaking into bullet points could improve readability, but it remains concise and effective.

    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 has only 2 parameters, no output schema, and no nested objects, the description is remarkably complete. It explains both parameters, the entire output (including verdict levels like CLEAN, LOW_RISK, etc.), and the tool's mechanism. No critical information is missing.

    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?

    Schema coverage is 100%, so baseline is 3. The description adds value by explaining that domains are resolved to IPs, and that deep=true queries additional DNSBL zones and fetches TXT listing reasons. This goes beyond 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 performs a live network and abuse reputation check on an IP or domain. It lists specific lookups (DNS, DNSBL, reverse-DNS) and return values (geolocation, ASN, BGP prefix, etc.). The purpose is distinct from the sibling tool check_many, which suggests batch operations.

    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 use cases: vetting inbound traffic, validating signups, moderating user-supplied IPs/domains. It does not explicitly state when not to use it or mention alternatives, but the context is sufficient for an agent to decide.

    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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  • Evaluate tool definition quality.

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