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stat-guy

Retrieval-Augmented Thinking MCP Server

by stat-guy
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JavaScript

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or misselection. The tool's purpose, while broad, is clearly the sole entry point.

    Naming Consistency2/5

    The single tool name 'rat' is vague and does not follow a clear verb_noun pattern or any recognizable convention. With only one tool, there's no consistent pattern to infer, and the name appears arbitrary.

    Tool Count3/5

    Exposing just one tool is on the lower end of acceptable, borderline 'thin.' Although the tool is highly capable, a server focused on 'Retrieval-Augmented Thinking' might benefit from separate tools for retrieval and reasoning sub-tasks.

    Completeness2/5

    The server name implies both retrieval and thinking, but the tool only covers the reasoning aspect, missing retrieval or external context fetching. This leaves a significant gap in the expected functionality, and the single tool is overloaded with parameters rather than modularly covering the domain.

  • Average 3.1/5 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 failing

  • 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?

    No annotations are provided, so the description carries full burden. It describes capabilities like branching and revisions but does not disclose side effects, statefulness, or what the tool returns when invoked. The behavior is described conceptually rather than practically.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Structured with sections and bullet points, making it skimmable. However, it is somewhat verbose with overlapping sections (Core Capabilities vs Reasoning Patterns). Could be tightened without losing meaning.

    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?

    The description is thorough about reasoning patterns but omits what the tool actually returns or how the agent should interpret the result. With no output schema and no annotations, this is a significant gap for correct invocation and result handling.

    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?

    Schema covers all 9 parameters with basic descriptions. The description adds value by categorizing what each parameter supports (e.g., 'thought' supports hypothesis formulation, branch exploration, etc.) and providing domain context for fields like branch_id and is_revision.

    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?

    Clearly states it is a context-aware reasoning system that orchestrates structured thought processes. The verb 'orchestrates' and resource 'thought processes' are explicit, but the tool's operational purpose for an agent is somewhat abstract; no siblings to differentiate from.

    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?

    Provides an Execution Protocol with steps, implying a multi-step reasoning workflow. However, it does not explicitly state when to use this tool versus other tools, as there are no siblings, nor does it give conditions for non-use.

    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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