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vanppsa

oracle-models

by vanppsa
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TypeScript
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Server Quality Checklist

67%
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  • Latest release: v2.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: task classification, plan block formatting, and model suggestions. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: classify_task, format_plan_block, get_model_suggestions.

    Tool Count4/5

    With only 3 tools, the server is near the lower end of the well-scoped range. It covers its intended niche adequately but feels slightly thin for a server named 'oracle-models'.

    Completeness3/5

    The tools cover classification, plan formatting, and model suggestions, but lack direct model invocation or detailed model information, which are notable gaps for a model-oriented server.

  • 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
    • 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 is passing

  • 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

  • Behavior3/5

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

    No annotations provided. Description states generation but lacks details on side effects or idempotency. However, tool is straightforward, so minimal transparency is acceptable.

    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?

    Single sentence, no wasted words. Could add minor context, but effective and front-loaded with 'MANDATORY'.

    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?

    With 5 parameters, low schema coverage, and no output schema or annotations, the description fails to provide sufficient detail for correct invocation, especially for parameter values and output format.

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

    Parameters2/5

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

    Schema coverage is only 20% (only preferred_provider has description). Description does not explain tier, reason, estimated_files, or estimated_tokens, leaving their meaning unclear.

    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 generates a formatted output block for plan end, distinguishing it from siblings like classify_task and get_model_suggestions.

    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 says 'MANDATORY' and 'to be pasted at the end of every plan', indicating when to use. No explicit when-not-to or alternatives, but given mandatory nature, it's sufficient.

    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?

    No annotations are provided, so the description bears the full burden of disclosure. It reveals the translation requirement and confirms the classification behavior, but does not disclose the nature of the output, side effects, or any rate limits. This is minimally 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?

    The description is extremely concise: one sentence with a parenthetical example. It is front-loaded with the core purpose and includes essential usage instruction without any fluff.

    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 tool is simple with 3 parameters and no output schema. The description explains the core purpose and a translation rule, but does not specify what the classification output looks like or any error conditions. It is adequate but could be more thorough.

    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 significant value by instructing the agent to translate non-English input for the 'description' parameter, which is a behavioral constraint not present in the schema. This enhances the semantic understanding.

    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: 'Classifies the complexity of a dev task.' It uses a specific verb ('classifies') and resource ('complexity of a dev task'), and the sibling tools (format_plan_block, get_model_suggestions) have distinct purposes, so there is no confusion.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus its siblings or alternatives. It includes a mandatory instruction about translating non-English input, but does not explain when not to use the tool or any contextual prerequisites.

    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 fully covers behavioral aspects: auto-detection of client environment, filtering logic for native vs. aggregator clients, and optional provider highlighting. No destructive actions or rate limits are mentioned, which is appropriate for a read-only suggestion 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?

    Three concise sentences: purpose, auto-detection behavior, and filtering detail. No redundant information. Front-loaded with the main action.

    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?

    Given the simple input schema and no output schema, the description adequately covers the tool's behavior and parameter usage. It could mention the output format (e.g., list of model names) but the current level is sufficient for selection and invocation.

    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 enum descriptions. The description adds value beyond the schema by explaining that preferred_provider is auto-detected if omitted and that tier affects the suggestion logic. This supplements the schema effectively.

    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 'Returns suggested models for a tier', which is a specific verb and resource. It further explains auto-detection of client environment and filtering logic, differentiating it from sibling tools like classify_task and format_plan_block.

    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 context: native clients receive only their provider's models, aggregator clients receive top 4 across providers. It also advises using preferred_provider to highlight a specific provider. It lacks explicit when-not-to-use instructions but is generally helpful.

    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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