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J-X0

westmere-recsys

by J-X0
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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: recommend generates candidate rankings under budget, record_feedback logs observed rewards, and budget_status reports spend/remaining cap. There is no meaningful overlap between them.

    Naming Consistency3/5

    The names are readable but not fully consistent: 'record_feedback' follows a verb_noun pattern, 'recommend' is a bare verb, and 'budget_status' is noun_noun without an action verb. A consistent set like 'recommend_items', 'record_feedback', and 'get_budget_status' would improve predictability.

    Tool Count5/5

    Three tools is a well-scoped size for a focused recommendation/bandit service. Each tool covers a necessary part of the core workflow: recommending, recording feedback, and checking budget.

    Completeness4/5

    The core loop of recommend -> record_feedback -> check budget is covered, and there are no dead ends in that workflow. However, there is no tool for managing tenants, candidate items, or budget configuration, which are minor gaps if the server is expected to handle those resources.

  • Average 2.8/5 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 status not available

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  • 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?

    With no annotations, the description carries the full burden of behavioral disclosure. It implies a read-only report and mentions the output concepts (spend and remaining cap), but it does not explain month defaults, response format, error behavior, or access requirements.

    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 one concise sentence with no filler, front-loading the key action and subject. It is easy to parse, though the brevity contributes to missing details elsewhere.

    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?

    There is no output schema and no annotations, so the description needs to explain return values and behavior more fully. It does not describe what the report looks like, what happens if no month is provided, or any edge cases, leaving the agent under-informed.

    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 description coverage is 0%, so the description must compensate for the lack of parameter documentation. It hints that tenant_id identifies the tenant and month selects the month, but it does not clarify formats, the optionality of month, or the exact meaning of 'remaining cap'.

    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 uses a specific verb ('Report') and a clear resource ('a tenant's monthly spend and remaining cap'), so an agent can understand the core function. It does not explicitly distinguish from sibling tools, but the resource is distinct enough from 'recommend' and 'record_feedback'.

    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?

    No guidance is given on when to use this tool versus the sibling tools, nor any context about prerequisites or typical scenarios. The description only states what the tool does, leaving the agent to infer when it should be invoked.

    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. It does not disclose whether ranking has side effects, how the spend cap is enforced, what happens when the cap is exceeded, or any rate/ordering behavior. The behavioral profile is largely unknown.

    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 a single, well-structured sentence with no filler. It front-loads the core purpose, though its brevity leaves key behavioral and parameter details uncovered.

    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?

    For a tool with 4 parameters, nested objects, no output schema, and no annotations, this description is too sparse. It lacks return-value expectations, parameter semantics for k and features, and any note on how ranking output is structured.

    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?

    With 0% schema description coverage and 4 parameters, the description only implicitly maps to tenant_id and candidates, while k and features are completely unaddressed. It does not compensate for the schema gap.

    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?

    States a clear action — 'Rank candidate items' — with a specific resource ('a tenant') and a constraint ('monthly spend cap'). It is distinguishable from sibling tools like record_feedback and budget_status, though it does not explicitly name them.

    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 phrase 'for a tenant under the monthly spend cap' implies the context in which this tool is appropriate, but there is no explicit when-to-use or when-not-to-use guidance, and no alternative tools are named.

    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, the description carries the full burden of behavioral disclosure. It adds the reward range [0,1], which is useful, but it does not state whether the operation is append-only, idempotent, requires an existing arm, or has any side effects on the recommendation model. For a write operation, 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?

    A single sentence that is front-loaded with the verb and includes the essential constraint. Every word adds meaning; there is no fluff or redundancy.

    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?

    For a tool with no annotations, no output schema, and no usage guidance, the description is too sparse. It defines the basic operation but leaves out the surrounding workflow, return behavior, and any consequences of calling it, which are needed for reliable invocation in context.

    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 0%, so the description must explain the parameters. It conveys that arm_id refers to an arm and reward is a value in [0,1], covering both parameters at a basic level. However, it lacks details about how arm_id is obtained or what happens with out-of-range rewards.

    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 ('Record'), the object ('an observed reward'), and the target ('for an arm'), making the tool's function immediately obvious. It does not explicitly differentiate from siblings, but the contrast with 'recommend' and 'budget_status' is conceptually clear.

    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 gives no guidance on when to use this tool versus the siblings. It does not mention that it should follow a 'recommend' call, nor does it explain the workflow context, leaving the agent to infer the appropriate usage.

    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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