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PovedaAqui

SuzieQ MCP Server

by PovedaAqui
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: run_suzieq_show performs a 'show' query to retrieve data, while run_suzieq_summarize performs a 'summarize' query to aggregate data. Their descriptions explicitly differentiate between querying and summarizing operations, leaving no ambiguity about which tool to use for each task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'run_suzieq_' as a prefix, followed by the specific operation ('show' or 'summarize'). This naming convention is predictable and helps users understand the tools' functions at a glance, with no deviations or mixed styles.

    Tool Count2/5

    With only two tools, the server feels thin for its apparent scope of network monitoring and analysis via SuzieQ. While the tools cover basic query and summarize operations, the domain suggests a need for more comprehensive functionality, such as additional query types or data manipulation tools, making the count insufficient for robust agent workflows.

    Completeness2/5

    The tool surface is severely incomplete for network monitoring and analysis. It lacks essential operations like data filtering beyond basic queries, configuration management, or integration with other network tools. The two tools provide only a minimal subset of what a full SuzieQ interface would offer, leaving significant gaps that will hinder agent effectiveness.

  • Average 3.4/5 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

  • 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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden. It mentions the tool runs via REST API and returns JSON or error messages, but lacks details on authentication needs, rate limits, side effects, or what 'summarize' entails behaviorally (e.g., aggregation, statistics). This is a significant gap for a tool with no annotation coverage.

    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 appropriately sized and front-loaded with the core purpose. The Args and Returns sections are structured clearly, though the 'filters' explanation is slightly verbose. Most sentences earn their place by adding value, with minimal redundancy.

    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?

    Given 2 parameters, no annotations, no output schema, and moderate complexity, the description covers purpose and parameters well but lacks behavioral context and explicit usage guidelines. It is adequate as a minimum viable description but has clear gaps in transparency and guidance.

    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 description coverage is 0%, so the description must compensate. It effectively adds meaning by explaining 'table' as the SuzieQ table name with examples and 'filters' as an optional dictionary with examples and usage notes. This goes beyond the schema's minimal titles, providing practical context for both parameters.

    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 'runs a SuzieQ summarize query via its REST API', specifying the verb (runs), resource (SuzieQ summarize query), and mechanism (REST API). It distinguishes from the sibling tool 'run_suzieq_show' by focusing on 'summarize' queries rather than 'show' queries, though the distinction could be more explicit.

    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 usage for SuzieQ summarize queries but does not explicitly state when to use this tool versus the sibling 'run_suzieq_show' or other alternatives. It provides context about the REST API mechanism but lacks explicit guidance on scenarios or prerequisites for choosing this tool.

    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 full burden for behavioral disclosure. It mentions the REST API mechanism and error handling in returns, but doesn't cover important aspects like rate limits, authentication needs, timeout behavior, or what constitutes valid table names beyond examples. For a tool with no annotation coverage, this leaves significant gaps in understanding operational constraints.

    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 well-structured with clear sections (Args, Returns) and uses bullet-like formatting for parameter details. While somewhat verbose, each sentence adds value by explaining parameter usage. The front-loaded purpose statement is clear, though some details could be more concise.

    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?

    Given the tool has no annotations, no output schema, and 2 parameters, the description does a good job with parameter semantics but lacks completeness in other areas. It doesn't explain the return structure beyond 'JSON string', doesn't cover error scenarios comprehensively, and omits behavioral constraints. For a query tool with REST API dependencies, more operational context would be helpful.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage, the description fully compensates by providing comprehensive parameter documentation. It clearly explains both parameters: 'table' with specific examples and 'filters' with detailed syntax, format examples, and handling of optional/null values. The description adds substantial meaning beyond what the bare schema provides.

    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 ('Runs a SuzieQ show query') and mechanism ('via its REST API'), providing a specific verb+resource combination. It distinguishes from the sibling tool 'run_suzieq_summarize' by specifying this is for 'show' queries rather than 'summarize' operations, though it doesn't explicitly contrast them in the text.

    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 usage context through the examples of tables and filters, suggesting when to use this tool for querying network data. However, it lacks explicit guidance on when to choose this over 'run_suzieq_summarize' or other alternatives, and doesn't mention prerequisites like API connectivity or authentication requirements.

    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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