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packagerating

packagerating MCP Server

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Server Quality Checklist

58%
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  • Latest release: v0.1.4

  • Disambiguation5/5

    Each tool has a distinct purpose: listing scored packages, fetching a single package's score, and enqueueing crawls without waiting. Although get_package can trigger a crawl, the key difference is that request_crawl is for background pre-warming, so there is no ambiguity.

    Naming Consistency5/5

    All tool names follow the same verb_noun pattern: list_packages, get_package, request_crawl. The naming is predictable and consistently formatted.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to its purpose of providing package ratings. Each tool is necessary and there is no bloat, making the count feel intentional rather than thin.

    Completeness5/5

    The tool surface covers the core workflows: listing available packages, retrieving detailed scores, and requesting background crawls for pre-warming. There are no obvious operational gaps for the stated domain.

  • Average 4.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 21 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
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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

  • Behavior3/5

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

    No annotations are provided, so the description carries the transparency burden. It discloses key filtering behavior (only packages with at least one score) and default sorting, but it does not mention the response format, pagination, or explicitly state that this is a read-only operation beyond the verb 'List'. There is no contradiction with any structured metadata.

    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 front-loads the core action and adds relevant qualifiers without any filler. Every word contributes to understanding the tool's purpose.

    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 complete parameter schema and simple list operation, the description adequately covers the key behavior: what is listed, the score filter, and default ordering. The lack of an output schema is somewhat mitigated by the straightforward nature of a list tool, though explicit return structure or pagination details would improve completeness.

    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?

    The input schema provides 100% coverage with full descriptions and enums for all four parameters, so the baseline is 3. The description adds minor context about 'composite score' and default sort, but it does not meaningfully expand on parameter usage beyond what the schema already declares.

    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 uses a specific verb ('List') and resource ('npm/PyPI packages') with a clear scope ('that have at least one score') and default ordering ('sorted by composite score by default'). This distinguishes it from siblings like get_package (single package lookup) and request_crawl (initiating crawls).

    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?

    It provides clear context for when to use the tool: when you need a list of scored npm/PyPI packages. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to distinguish this list operation from package details or crawling requests.

    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 present, so the description carries the burden. It discloses that the operation is non-blocking ('without waiting for the result') and that it enqueues, but it does not clarify return behavior, authentication needs, or queue side effects, leaving some ambiguity.

    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 two sentences, front-loaded with the core action, and every clause contributes value. No wasted words or repetition of schema fields.

    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 simple tool with 2 parameters and no output schema, the description covers the main behavior and a use case. It lacks explicit mention of what the caller receives after enqueueing, but given the tool's low complexity, it is reasonably complete.

    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?

    The schema already provides 100% coverage of both parameters with descriptions, so the description adds little beyond reinforcing that multiple packages can be submitted ('one or more'). It does not introduce new constraints or format details beyond the 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 'Enqueue' and the resource 'packages for crawling,' and distinguishes itself from siblings by emphasizing 'without waiting for the result,' which contrasts with synchronous retrieval tools like get_package.

    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?

    It provides a clear use case: 'pre-warming several packages at once, e.g. before comparing alternatives,' which implies when this tool is appropriate. However, it does not explicitly name alternatives or state when not to use it, so it falls short of a full 5.

    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 burden and does disclose the non-obvious side effect: triggering a crawl and waiting bounded if never scored. It doesn't cover timeout behavior, output shape, or idempotence, but the key behavioral trait is clearly stated.

    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 focused sentences: the first states the purpose, the second a necessary caveat about triggering a crawl. No redundant words or repetition of schema details.

    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 no annotations and no output schema, it covers the main purpose, side-effect, and bounded wait. Missing details are mostly output structure and explicit relationships to siblings, but the description is sufficient for correct selection and basic invocation.

    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% and all parameters have descriptive metadata (name, version, language). The description adds 'by name' and 'full score' but does not provide meaning beyond the schema, matching the baseline for high schema coverage.

    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 uses a specific verb ('Get') with a clear resource ('full health/risk score for a single package by name'). It distinguishes from sibling tools by emphasizing 'single package' and the score result, versus list_packages (listing) and request_crawl (triggering crawls).

    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?

    It implies when to use: when you need a score for one package, and warns that it may trigger a crawl if unscored. However, it does not explicitly name alternatives like request_crawl for cases where you don't want to wait, so it stops short of full when/when-not guidance.

    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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