crash-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap. The tool's purpose of recording structured reasoning steps is clearly distinct and unambiguous.
Naming Consistency5/5Since there is only one tool, there are no naming inconsistencies to evaluate. The name 'crash' is somewhat unconventional but does not conflict with any other tools.
Tool Count3/5A single tool feels thin for a server, especially one named 'crash-mcp' which hints at broader scope. However, for the narrow purpose of logging reasoning steps, one tool could be sufficient, making it borderline.
Completeness4/5The tool covers the full lifecycle of recording a reasoning step, including parameters for branching, revising, confidence, and final-step indication. It lacks explicit retrieval or management capabilities, but the domain is narrow enough that the tool is effectively complete.
Average 4.3/5 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 status not available
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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 transparency burden. It explains the recording behavior, revision marking, branching, and finalization, and explicitly discloses the return format: 'Returns JSON summary with step count, completion status, and next action.' It also notes session expiration via the schema. This is sufficient for an agent to understand the tool's effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear intro, 'WHEN TO USE' bullets, and a numbered workflow, making it scannable despite its length. It is front-loaded with the purpose statement. Some redundancy exists between workflow steps and schema descriptions, but for 20 parameters the level of detail is appropriate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 20 parameters, no output schema, and no annotations. The description provides the essential context: purpose, use cases, a step-by-step workflow, and a description of the return value. It covers branching, revision, confidence, and completion status. While examples are not provided, the combination of description and schema is sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% parameter descriptions, giving a baseline of 3. The description adds value by explaining how parameters are used together in the workflow: 'Start with step_number=1', 'Use confidence (0-1)', 'Use revises_step to correct', 'Use branch_from to explore', and 'Set is_final_step=true'. This inter-parameter guidance goes beyond the standalone schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The first line states the tool's function precisely: 'Record a structured reasoning step for complex problem-solving.' It uses a clear verb ('Record') and resource ('structured reasoning step'), and the 'WHEN TO USE' section further clarifies its scope. Despite the misleading tool name 'crash', the description leaves no doubt about the tool's purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
A dedicated 'WHEN TO USE' section lists concrete scenarios: multi-step analysis, debugging, planning, systematic exploration, and branching. It also provides a numbered workflow for how to invoke the tool across a reasoning session. However, it does not explicitly state when not to use the tool or mention alternatives, though no sibling tools exist.
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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