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ponthepmk

ai-discuss

by ponthepmk
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: discuss runs the debate, list_models discovers available models, and list_participants shows configured participants. No functional overlap.

    Naming Consistency5/5

    All tools use consistent snake_case verb_noun naming (discuss, list_models, list_participants), with a clear pattern.

    Tool Count5/5

    Three tools is well-scoped for this focused server; each tool plays an essential role without redundancy.

    Completeness3/5

    Tools cover the main discussion task and listing of resources, but lack create/update/delete operations for participants or models, leaving configuration management to external means.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 2 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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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

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It discloses that the tool writes a transcript to disk (side effect) and outlines the debate process. Could mention permissions or error handling, but current detail is sufficient for basic understanding.

    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 with no redundancy. First sentence covers core process and output, second notes transcript. Every word earns its place.

    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 tool with 7 parameters and an output schema, description explains workflow, output (ranked recommendation), and side effect (transcript). Lacks explicit mention of what output schema contains, but output schema covers that. Adequate given complexity.

    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 baseline is 3. Description adds context by mapping 'topic+code context' to specific parameters and mentioning 'N-round debate' for rounds, but does not significantly enhance schema-provided meanings.

    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 runs a multi-agent debate with specific actions: fan out topic+context, run N-round debate, critique/refine, return ranked recommendation, and write transcript. Distinguishes from sibling listing tools (list_models, list_participants).

    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?

    Description implies use for multi-agent discussion/debate but lacks explicit guidance on when not to use or alternatives. Siblings are listing tools, so context is clear, but no direct usage constraints provided.

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

  • Behavior4/5

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

    With no annotations, the description carries the full burden. It discloses the action (query), the scope (all configured providers by default), and the output (model ids). It does not discuss edge cases like unreachable providers or caching, but for a simple query tool, the disclosure is 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: first explaining the action, second providing the use case. No unnecessary words, front-loaded, efficient.

    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?

    Though there is an output schema (not shown), the description does not need to detail return values. It covers purpose, usage context, and parameter behavior adequately for a simple tool. Minor lack of details about error states or configuration prerequisites, but still complete enough.

    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 optional parameter 'provider', with a clear description in the schema. The tool description adds no new parameter details beyond restating that it queries each configured provider by default, so no additional value over schema.

    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 'Query' and the resource 'each configured provider for the model ids'. It also provides a use case ('discover valid model names before editing the config or choosing participants'), distinguishing it from sibling tools like 'discuss' and 'list_participants'.

    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 usage context: 'Useful to discover valid model names before editing the config or choosing participants.' It does not explicitly state when not to use or alternatives, but the context is clear and distinguishes from siblings.

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

  • Behavior4/5

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

    No annotations exist, so description must carry behavioral disclosure. It details output fields and implies read-only operation. It could mention permissions or side effects but is adequate for a list tool.

    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 with no redundancy. First sentence states purpose and output; second provides usage context. Every word earns its place.

    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?

    With an output schema (existing but not shown), description does not need to detail return values. It covers the tool's purpose, output fields, and usage hint, making it complete for an agent.

    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% for the single parameter, so description adds no extra meaning beyond schema. Baseline score of 3 is appropriate.

    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 lists AI participants, specifying the fields returned (type, model, enabled/available status, default synthesizer). It distinguishes from sibling tools by noting it is useful before calling discuss, implying preparation.

    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?

    Description explicitly states 'Useful before calling discuss,' giving clear context for when to use. It does not exclude other use cases or compare to list_models, but the guidance is sufficient for an agent.

    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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