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jerpint

paperpal

by jerpint
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one fetches details for specific Arxiv papers by ID, while the other performs semantic searches on HuggingFace. There is no overlap in functionality or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent snake_case naming pattern with descriptive verb_noun structures (fetch_paper_details_from_arxiv and semantic_search_papers_on_huggingface). The naming is predictable and readable throughout.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a paper-related domain. It lacks basic operations like listing papers, filtering, or managing collections, which limits its utility for comprehensive paper handling tasks.

    Completeness2/5

    The toolset is severely incomplete for paper management. It covers fetching details and searching, but misses essential CRUD operations (e.g., saving, organizing, or annotating papers) and lacks integration between the two sources, leaving significant gaps in workflow coverage.

  • Average 3.3/5 across 2 of 2 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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 the tool fetches info but doesn't describe key behaviors like whether it's a read-only operation, error handling for invalid IDs, rate limits, or authentication needs. This is a significant gap for a tool with no annotation coverage.

    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 appropriately sized and front-loaded, with the main purpose stated first and parameter details following. It uses two sentences efficiently, with no wasted words, making it easy to parse quickly.

    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's low complexity (1 parameter) and the presence of an output schema, the description is somewhat complete but has gaps. It covers the basic purpose and parameter usage but lacks behavioral details and usage guidelines. With an output schema, it doesn't need to explain return values, but overall it's only minimally adequate.

    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 description adds meaningful semantics beyond the input schema. The schema has 0% description coverage, but the description explains that 'arxiv_ids' can be a list or string and provides an example (e.g., ["2503.01469", "2503.01470"]), clarifying usage. With only one parameter, this compensates well for the low schema coverage.

    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 tool's purpose: 'Get the Arxiv info for a list of papers.' It specifies the verb ('Get') and resource ('Arxiv info'), making it understandable. However, it doesn't explicitly differentiate from the sibling tool 'semantic_search_papers_on_huggingface', which appears to be a search tool rather than a direct fetch by ID, so it misses full sibling distinction.

    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 alternatives. It doesn't mention the sibling tool or any other context for usage, such as prerequisites or scenarios where this tool is preferred over others. This leaves the agent without explicit direction on tool selection.

    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 carries the full burden of behavioral disclosure. It mentions that the query 'will automatically determine if it should use keywords or a natural language query,' which adds some context about the tool's behavior. However, it lacks details on rate limits, authentication needs, error handling, or what happens with invalid inputs, which are important for a search tool.

    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 appropriately sized and front-loaded, with the core purpose stated first. The Args and Returns sections are structured clearly, though the 'Returns' section could be more concise (e.g., listing fields without full sentences). Overall, it's efficient with minimal waste.

    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 tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is reasonably complete. It explains the parameters and return format, and the output schema likely covers the return structure in detail. However, it could benefit from more behavioral context (e.g., search scope, limitations) to be fully comprehensive.

    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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'query' can be keywords or natural language and will be automatically interpreted, and it specifies the default and flexibility for 'top_n'. This compensates well for the schema's lack of descriptions, though it doesn't cover all possible edge cases (e.g., query length limits).

    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 tool's purpose: 'Search for papers on HuggingFace using semantic search.' This specifies the verb (search), resource (papers on HuggingFace), and method (semantic search). However, it doesn't explicitly differentiate from the sibling tool 'fetch_paper_details_from_arxiv' (which appears to fetch details rather than search), so it doesn't reach the highest score.

    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 alternatives. While it mentions semantic search, it doesn't explain when to prefer this over keyword-based search or the sibling tool. There's no mention of prerequisites, limitations, or typical use cases, leaving the agent with minimal context for selection.

    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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