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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined as an intelligent academic paper search system, making it impossible for an agent to misselect between non-existent alternatives.

    Naming Consistency5/5

    The single tool name 'paper_search' follows a clear verb_noun pattern (search as the verb, paper as the noun). Since there are no other tools to compare against, consistency is inherently perfect with no deviations or mixed conventions.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and may indicate an incomplete or overly narrow scope. While the tool is described as advanced, a server focused on academic paper retrieval would typically benefit from additional tools (e.g., for filtering, citation analysis, or paper details) to provide comprehensive coverage.

    Completeness2/5

    The server's domain appears to be academic paper retrieval, but with only a search tool, there are significant gaps. For example, there are no tools for accessing paper metadata, downloading papers, managing searches, or handling citations, which are common needs in this domain. This incomplete surface will likely cause agent failures when trying to perform full research workflows.

  • Average 2.9/5 across 1 of 1 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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions features like 'multi-dimensional relevance scoring' and 'semantic search,' it doesn't describe what the tool actually returns, how results are presented, pagination behavior, rate limits, authentication requirements, or error handling. The description is feature-focused rather than behaviorally transparent.

    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, efficient sentence that communicates the core functionality and target audience without unnecessary words. It's appropriately sized for the tool's complexity, though it could be more front-loaded with the primary action ('search academic papers') rather than starting with the system type.

    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 complex tool with 10 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns (paper metadata, full text, links), how results are structured, or provide behavioral context needed for effective use. The feature list doesn't compensate for these significant gaps in operational understanding.

    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 description coverage is 100%, with all 10 parameters well-documented in the schema itself. The description adds no specific parameter information beyond what's already in the schema, so it meets the baseline of 3. The description's mention of 'multi-dimensional relevance scoring' and 'semantic search' relates to overall functionality rather than parameter semantics.

    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 as an 'intelligent academic paper search system' with specific features like multi-dimensional relevance scoring, academic quality assessment, and semantic search. It identifies the target users as 'researchers and professors' and mentions advanced functionality, but doesn't differentiate from siblings since none exist.

    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, prerequisites, or specific scenarios where it's most appropriate. It only mentions the target audience without operational context. With no sibling tools, this is less critical but still a gap in usage 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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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