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cktbarking

semantic-scholar-mcp

by cktbarking
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one searches for academic papers, the other downloads a PDF by DOI. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow the verb_noun pattern (search_papers, download_paper), which is consistent, predictable, and easy to understand.

    Tool Count2/5

    With only 2 tools, the server feels significantly under-scoped for a service like Semantic Scholar. A typical integration would include many more capabilities, such as retrieving paper metadata, authors, citations, and recommendations, making this count too low for the domain.

    Completeness2/5

    The tool surface covers only search and PDF download by DOI. Major functionalities like fetching paper details by ID, exploring citations/references, and author information are missing, leaving obvious gaps that could cause agent failures.

  • Average 3.7/5 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 GPL 3.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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. It only repeats the parameter info from the schema and states the core function, adding no behavioral details such as what is returned, whether the operation is read-only, or any side effects. 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?

    The description is extremely concise, with the main purpose in one short sentence followed by clear parameter documentation. It is front-loaded and contains no unnecessary words or repetition.

    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?

    Given the lack of an output schema, the description should explain what the tool returns or other search behavior. It does not mention return format, pagination, or search syntax. The description covers only the basic function and parameters, leaving the agent guessing about the tool's full behavior.

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

    Parameters5/5

    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. It does so effectively by explaining both parameters: 'query' is the search query string, and 'limit' is the number of results with default and max values. This adds meaning beyond the schema's type/default information.

    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 action with a specific verb and resource: '搜索学术论文' (search academic papers). This distinguishes it from the only sibling tool, download_paper, which has a different purpose. The tool name also aligns with the description.

    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 like download_paper. It does not mention use cases, prerequisites, or exclusions. The only context is the purpose statement itself.

    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 must carry the full burden. It discloses that the tool saves to an output directory and returns download result info, but it does not mention potential issues such as access restrictions, network requirements, file overwrite behavior, or failure handling. This is insufficient for a file-downloading 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?

    The description is concise and front-loaded with the core purpose. It includes parameter and return documentation without any fluff or repetition. Every sentence contributes useful information.

    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 two parameters and no output schema, the description covers the purpose, parameters, and return value. It lacks explicit error-handling or edge-case context, but given the tool's simplicity, it is reasonably complete.

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

    Parameters5/5

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

    The description explicitly explains both parameters: doi is the paper's DOI identifier, and output_dir is the optional PDF output directory. This adds meaningful context beyond the schema, which only provides titles and defaults. The schema description coverage is 0%, but the description fully compensates.

    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 action: download a paper's PDF based on DOI. It uses a specific verb (download) and resource (paper PDF), and distinguishes itself from the sibling tool search_papers by focusing on downloading rather than searching.

    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 description implies the tool is used when you have a DOI and want the PDF, but it does not explicitly mention when to use it versus alternatives, nor does it provide any exclusions or prerequisites. There is no comparison with the sibling tool search_papers.

    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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