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divenire990

google-scholar-labs-ajg-mcp

by divenire990

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    With only a single tool, there is no possible confusion between competing choices. The tool's purpose is clear and distinct by default.

    Naming Consistency5/5

    The name `scholar_labs_search` follows a consistent domain/action pattern. With only one tool, there are no naming conflicts or inconsistencies to evaluate.

    Tool Count4/5

    A single tool is at the low end of the typical range, but it provides a comprehensive search-and-filter operation for a narrowly scoped server. It is slightly under the usual 3-15 tools yet reasonable for this focused purpose.

    Completeness5/5

    The tool covers the full search workflow including AJG filtering, exclusion records, and authentication/CAPTCHA handoff. Within the stated domain of AJG-filtered Google Scholar search, there are no obvious missing operations.

  • Average 3.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden and largely succeeds: it discloses the logged-in-session requirement, the AJG strict-filtering behavior, and the CAPTCHA/manual-handoff scenario. It adds context beyond what structured fields offer, though it stops short of mentioning rate limits or failure modes beyond CAPTCHA. No contradiction with annotations exists since none are present.

    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?

    Three sentences, each earning its place: purpose, return behavior, and fallback handling. The primary purpose is front-loaded in sentence one. No filler or redundancy. Slightly more could be trimmed but it is appropriately tight for a tool of this complexity.

    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 complex browser-automation tool with 9 parameters, no output schema, and no annotations, the description covers the core workflow (search, AJG filtering, return of qualifying/excluded records) and the critical handoff path. It lacks an exact return-format spec, but the high-level return description partially compensates for the missing output schema.

    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 100%, so all nine parameters are already documented in the schema with types, defaults, and descriptions. The tool description adds no additional parameter-level detail beyond restating the ABS2+ default that min_stars already encodes. Baseline 3 applies; the schema does the heavy lifting.

    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 states a specific verb and resource ('Search Google Scholar Labs through a logged-in CloakBrowser session') and adds the distinctive filtering behavior ('filter results strictly against the AJG 2024 rankings'). It also specifies the return scope (qualifying papers plus exclusion records). Clear, specific, and unambiguous even without siblings to differentiate.

    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 states what the tool does and notes the manual-handoff path for CAPTCHA or login, which gives context on when a human may need to step in. However, with no sibling tools listed and no explicit when-to-use vs when-not-to-use statements, the usage guidance is implicit rather than directive. The handoff note is a behavioral fallback, not a usage-exclusion rule.

    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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  • Confirm that the MCP server is working as expected.
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google-scholar-labs-ajg-mcp MCP server – quality and maintenance score on Glama

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