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raj-rubicon

Rubiscout Email Analysis MCP Server

by raj-rubicon
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Server Quality Checklist

67%
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  • Latest release: v0.1.2

  • Disambiguation5/5

    Each tool targets a distinct operation: analyzing a new email, retrieving a specific analysis by UUID, and listing recent analyses. There is no functional overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with lowercase and underscores (analyze_email, get_analysis, list_analyses), making them predictable.

    Tool Count4/5

    With 3 tools, the server is scoped tightly to email analysis. While minimal, it covers the primary use cases without unnecessary bloat.

    Completeness4/5

    The server provides core CRUD-like operations (create, read, list) for email analyses. Missing update and delete are minor gaps, but the essential workflow for analysis is complete.

  • Average 3.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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?

    Description states ordering and filter behavior but omits pagination details (default page, next page handling) and safety (read-only). With no annotations, more behavioral disclosure would be beneficial.

    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 concise sentences, front-loaded with action and key ordering detail. No redundant or extraneous text.

    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?

    Adequately covers purpose, ordering, and filter for a simple list tool. Missing return format or pagination behavior details, but schema covers parameter constraints. Output schema absent, but completeness is acceptable given tool simplicity.

    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 descriptions fully cover all three parameters. Description only repeats risk_score filter, adding no additional meaning beyond schema. Baseline 3 as schema coverage is 100%.

    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?

    Clear verb 'list' specifies action, resource 'analyses' with ordering 'by most recent first' and optional filter. Distinguishes from sibling tools 'analyze_email' (create) and 'get_analysis' (single retrieval).

    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?

    No explicit when-to-use or when-not-to-use guidance. Sibling names imply context but description lacks directives on comparing with alternatives or prerequisites.

    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?

    Without annotations, the description carries the full burden. It discloses the output (forensic verdict with SPF/DKIM/DMARC, IP reputation, etc.) and input format, but does not mention side effects, storage, auth requirements, or statelessness. This leaves some behavioral gaps.

    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?

    Description is concise (two sentences), front-loads the purpose, and every sentence adds value. No wasted words.

    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?

    With one parameter, no output schema, and no annotations, the description covers input, process, and output adequately. It lacks prerequisites or error conditions but is complete enough for the tool's complexity.

    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% with one parameter fully described. The description adds meaning by clarifying that the 'email' parameter accepts raw headers or full RFC 2822 email, which goes beyond the schema's minimal 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?

    Description clearly states the tool's purpose: analyzing emails for phishing, spoofing, and authentication failures. It specifies acceptable input formats (raw headers or full RFC 2822) and distinguishes from sibling tools (get_analysis, list_analyses) by focusing on performing a new analysis rather than retrieving existing ones.

    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 implies using this tool when you have an email to analyze, but it does not explicitly state when to use it versus alternatives. No exclusions or when-not-to-use guidance is provided, leaving the agent to infer from sibling names.

    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?

    The description states the tool is read-only and returns specific fields (verdict, risk score, etc.). However, it does not disclose error behavior (e.g., missing UUID), rate limits, or authorization requirements. Without annotations, more detail on limitations would be beneficial.

    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-formed sentence that front-loads the verb and resource. It covers the purpose, key input, and return value without extraneous words.

    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 simple retrieval tool with one parameter and no output schema, the description fairly covers the return values (verdict, risk score, etc.). It lacks error handling details but is otherwise sufficient.

    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 covers 100% of parameters with a description. The tool description adds no new meaning beyond restating the UUID origin. Per guidelines, high schema coverage leads to 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 identifies the action (Retrieve), the resource (Rubiscout analysis), and the method (by UUID). It distinguishes itself from sibling tools like analyze_email (which creates) and list_analyses (which lists).

    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 implies when to use the tool (to fetch a specific analysis). However, it does not explicitly state when not to use it or mention prerequisites like needing a UUID from analyze_email, although that context is present in the schema.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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