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Ravicha2

io.github.Ravicha2/lit-review-council

by Ravicha2
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.6

  • Disambiguation5/5

    There is only one tool, so there is no chance of selecting between overlapping tools. The single tool's purpose is also clearly conveyed by its name and description.

    Naming Consistency5/5

    The tool name follows a clear verb_noun pattern: conduct_literature_review. With only one tool in the set, there are no naming inconsistencies to evaluate.

    Tool Count3/5

    A single tool is on the thin side, but it functions as a high-level workflow orchestrator for the entire literature review process. The count feels borderline rather than excessive or trivially mismatched.

    Completeness4/5

    The tool covers the core workflow from research question and topics to a generated output bundle, so there is no dead end. Minor gaps exist around auxiliary capabilities like validating topics in advance or checking the status of past reviews, but these are workable for a one-shot orchestration tool.

  • Average 4.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 18 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

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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

  • Behavior4/5

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

    With no annotations, the description carries the behavioral burden. It discloses that execution is multi-agent, writes output to a configurable directory, and returns a success message with the bundle path. This is meaningful behavioral context beyond a simple 'runs a review' statement.

    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 front-loaded with a one-sentence purpose summary, then organized into Args and Returns sections. Each sentence provides necessary information with no filler or repetition.

    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?

    The description covers all three parameters, required versus optional fields, topic structure, default behavior, and return format. It is missing minor context such as what 'OKF' means or any expectations about runtime/cost, but for invocation purposes it is sufficiently 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?

    Schema coverage is 0%, but the description fully compensates by documenting question, output_dir with default, and the required structure of each topics entry including slug, description, search_keywords, and optional rationale. This gives an agent everything needed to construct valid arguments.

    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 action ('Executes a multi-agent literature review') with clear inputs (research question, topics) and output (OKF markdown bundle). Even without siblings, it is unambiguous about what the tool does.

    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?

    The opening sentence makes the use case clear, and the Args section details what callers must supply. There are no siblings to differentiate from, so explicit alternatives are not required, though the description stops short of stating when not to use this tool.

    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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  • Evaluate tool definition quality.

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