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nikkoxgonzales

CRASH - Cascaded Reasoning with Adaptive Step Handling

by nikkoxgonzales
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'crash' has a clearly defined purpose for structured reasoning steps, so an agent cannot misselect between tools.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'crash' follows a single, consistent pattern with no deviations or mixing of conventions to evaluate.

    Tool Count2/5

    A single tool is too few for the server's purpose of 'Cascaded Reasoning with Adaptive Step Handling,' which implies a multi-step or complex workflow. While the tool is well-described, a single tool feels thin and incomplete for such a domain, limiting functionality and agent capabilities.

    Completeness2/5

    The tool set is severely incomplete for the stated purpose. Although the 'crash' tool supports recording reasoning steps, there are obvious gaps—such as tools for retrieving, updating, or analyzing recorded steps, or for managing the reasoning process (e.g., starting, pausing, or summarizing). This will likely cause agent failures in complex tasks.

  • Average 4.3/5 across 1 of 1 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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 of behavioral disclosure. It effectively describes the tool's workflow, including how to handle revisions, branching, confidence levels, and final steps, and mentions that it 'Returns JSON summary with step count, completion status, and next action.' However, it lacks details on error handling, performance limits, or data persistence.

    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 well-structured with clear sections ('WHEN TO USE', 'WORKFLOW'), front-loaded purpose, and no redundant sentences. However, it is moderately long due to the detailed workflow instructions, which are necessary but could be slightly condensed.

    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?

    Given the high complexity (20 parameters, no output schema, no annotations), the description is mostly complete, covering purpose, usage, workflow, and return format. It compensates well for the lack of annotations and output schema, but could benefit from more on error cases or limitations.

    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 the schema already documents all 20 parameters thoroughly. The description adds value by explaining the overall workflow and how parameters like 'step_number', 'confidence', and 'revises_step' are used in context, but it doesn't provide additional semantic details beyond what the schema specifies.

    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 purpose: 'Record a structured reasoning step for complex problem-solving' and 'break down multi-step problems into trackable reasoning steps.' It specifies the verb ('record') and resource ('reasoning step') with 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 Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The 'WHEN TO USE' section explicitly lists four scenarios for using this tool (e.g., 'Multi-step analysis, debugging, or planning tasks'), and the 'WORKFLOW' section provides detailed guidance on how to use it step-by-step, including when to adjust parameters like 'estimated_total' or use features like 'revises_step'.

    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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