Lucidity MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'analyze_changes' has a clearly defined and distinct purpose of preparing git changes for analysis, making it impossible to confuse with other tools.
Naming Consistency5/5The single tool name 'analyze_changes' follows a clear verb_noun pattern (analyze + changes). Since there is only one tool, there is no inconsistency to evaluate, and the naming is straightforward and descriptive.
Tool Count2/5A single tool is too few for most practical server purposes, as it limits functionality and scope. While the tool is well-defined, a server with only one tool feels thin and incomplete for handling git analysis or broader tasks, indicating a mismatch in scope.
Completeness2/5The server's domain appears to be git change analysis, but with only one tool that prepares data without performing analysis, there are significant gaps. Missing operations like actual analysis, history review, or commit management make the surface severely incomplete for the inferred purpose.
Average 3.5/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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- No high-severity vulnerability alerts
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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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool 'examines the current git diff' and 'formats the git diff data,' implying it's a read-only operation without side effects. However, it doesn't mention behavioral traits like error handling, performance considerations, or specific constraints (e.g., git repository must be initialized). It adds some context but isn't comprehensive.
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 appropriately sized and front-loaded, starting with the core purpose. It uses bullet points for Args and Returns, which improves structure. However, some sentences could be more concise (e.g., 'through the Model Context Protocol' is slightly redundant), and the overall flow is clear but not maximally efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 0% schema coverage, and no output schema, the description is moderately complete. It covers purpose, parameters, and return value at a high level, but lacks details on error cases, output structure, or operational constraints. For a tool with two parameters and no structured support, it's adequate but has gaps.
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?
Schema description coverage is 0%, so the description must compensate. It explains both parameters: 'workspace_root: The root directory of the workspace/git repository' and 'path: Optional specific file path to analyze.' This adds clear meaning beyond the schema's basic titles. However, it doesn't detail format constraints (e.g., absolute vs. relative paths), so it's not a perfect 5.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'examines the current git diff, extracts changed code, and prepares structured data with context for the AI to analyze.' It specifies the verb (examines, extracts, prepares) and resource (git diff/changed code). However, since there are no sibling tools, it doesn't need to distinguish from alternatives, so it doesn't reach the full 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating 'Prepare git changes for analysis through MCP' and 'The tool doesn't perform analysis itself,' which suggests it's a preprocessing step. However, it lacks explicit guidance on when to use it versus other potential tools (e.g., direct analysis tools) or prerequisites, as there are no siblings to compare against.
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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