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YuChenSSR

Multi-Model Advisor

by YuChenSSR
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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one lists available models, while the other queries models for responses. There is no overlap in functionality, making it easy for an agent to choose the correct tool for each task without confusion.

    Naming Consistency4/5

    Both tools use a verb_noun pattern (list-available-models and query-models), which is consistent and readable. However, the hyphenation in 'list-available-models' slightly deviates from the simpler 'query-models', but overall the naming is predictable and follows a clear convention.

    Tool Count2/5

    With only 2 tools, the server feels thin for its purpose of advising on multiple models. While the tools cover listing and querying, the scope suggests potential gaps in operations like model management or comparison analysis, making the count too low for a comprehensive multi-model advisory system.

    Completeness2/5

    The tool surface is significantly incomplete for a multi-model advisor. It lacks operations such as managing models (e.g., adding or removing models), comparing responses in a structured way, or handling model configurations. This will likely cause agent failures when trying to perform full advisory workflows beyond basic listing and querying.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • 1 of 1 community issues answered or closed 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.

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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 of behavioral disclosure. It states it 'List all available models' but doesn't describe what 'available' means (e.g., locally installed, remote, with status), how results are returned (e.g., format, pagination), or any constraints (e.g., permissions, rate limits). This leaves significant gaps in understanding the tool's behavior.

    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, efficient sentence that directly states the tool's purpose and references the sibling tool. It is front-loaded with the core action and resource, with no wasted words or unnecessary elaboration, making it highly concise and well-structured.

    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 0 parameters, no annotations, and no output schema, the description is minimally adequate by stating what it does. However, it lacks details on behavior (e.g., output format, what 'available' entails) and doesn't leverage the low complexity to provide more context, making it incomplete for fully informed use without additional assumptions.

    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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied as it effectively handles the lack of parameters without introducing confusion or redundancy.

    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 verb 'List' and the resource 'all available models in Ollama', which provides a specific purpose. It distinguishes from the sibling tool 'query-models' by indicating these models are 'used with' it, though it doesn't explicitly differentiate their functions beyond that implied relationship.

    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 by mentioning the sibling tool 'query-models', suggesting this tool is for discovering models to use with it. However, it lacks explicit guidance on when to use this tool versus alternatives or any prerequisites, leaving usage context somewhat inferred rather than clearly stated.

    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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions querying multiple models and getting responses for comparison, but fails to disclose critical behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, error handling, or the format of responses. For a tool with no annotations and complex functionality, this is a significant gap.

    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-structured sentence that efficiently conveys the core functionality without waste. It is front-loaded with the main action ('query multiple AI models') and includes essential context ('via Ollama', 'to compare perspectives'), making every word earn its place.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (querying multiple models with optional prompts), lack of annotations, and no output schema, the description is incomplete. It does not explain return values, error conditions, or behavioral constraints, leaving significant gaps for an AI agent to understand how to invoke and interpret results effectively.

    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 schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying the tool queries 'multiple' models and compares responses, but does not provide additional semantics, syntax, or format details for parameters. Baseline 3 is appropriate when the schema does the heavy lifting.

    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's purpose with specific verbs ('query multiple AI models', 'get their responses') and resources ('via Ollama'), and distinguishes it from the sibling tool 'list-available-models' by focusing on querying rather than listing models. It explicitly mentions the comparative aspect ('to compare perspectives'), which adds valuable context beyond basic querying.

    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 comparing model responses to a question, but provides no explicit guidance on when to use this tool versus alternatives (e.g., querying a single model) or any prerequisites. It mentions 'defaults to configured models' for the models parameter, which offers some contextual hint, but lacks clear when/when-not directives or named alternatives.

    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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