clenzer
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: scanning for dead code, cleaning dead code, scanning for complexity, reporting, and registering rules. No overlap.
Naming Consistency5/5All tool names use lowercase with underscores and follow a verb_noun pattern or single verb (cleanse, report). Consistent and predictable.
Tool Count5/5Five tools is well-scoped for a code hygiene assistant, covering scanning, cleaning, reporting, and setup without excess.
Completeness4/5Covers dead code detection and removal, complexity scanning, and reporting. Minor gap: no automatic fix for complexity issues (only scan).
Average 3.9/5 across 5 of 5 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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
This repository is licensed under ISC License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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 provided, so description must disclose behavioral traits. It does not mention whether the tool is read-only, destructive, or what side effects occur. The scan likely is non-destructive but never stated.
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?
Single sentence front-loaded with purpose, no wasted words. However, it sacrifices parameter and usage detail, which is acceptable for conciseness but impacts completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters and no output schema, description is incomplete. It does not explain return values, how to interpret results, or how exclude/include interact. For a scanning tool, this is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 33% (only project_root described). Tool description adds no detail about include/exclude parameters or how they filter the scan. The description does not compensate for low schema coverage.
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?
Description clearly states 'Scans for unnecessary complexity' and lists specific types (overly long functions, deep nesting, large files, duplicate code blocks). This verb+resource is distinct from sibling tools like scan_dead_code.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, no prerequisites, and no context on expected input or output format. The description simply states what it does.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral traits. It mentions 'compact, token-efficient' (performance) and lists content, but fails to disclose whether the tool is read-only (likely), requires any authentication, or has side effects. The description lacks key transparency for a safe inference.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action ('Returns a compact summary'), then specifics. Every word adds value with no redundancy or filler.
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?
For a simple summarization tool with no output schema, the description adequately covers the main purpose and contents. However, it assumes a 'last scan' exists without mentioning preconditions or how multiple scans are handled, slightly reducing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters (project_root, top_n). The description adds no additional meaning beyond what the schema provides, meeting the baseline for high coverage but not exceeding it.
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 description clearly states the tool returns a compact summary of findings from the last scan, listing specific contents like dead code count, complexity hotspots, and actionable next steps. This distinguishes it from siblings like scan_dead_code and scan_complexity, which perform individual scans rather than summarize.
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 the tool should be used after a scan has been run ('from the last scan'), but does not explicitly state when to use it versus alternatives, nor does it provide when-not guidance. With siblings including multiple scanning tools, it could be clearer which scan results are aggregated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: it auto-removes unused imports and side-effect-free variables, and skips functions, exports, and initializers with side effects. It also mentions the dry_run parameter for preview. This provides a clear understanding of what the tool does and does not modify.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences. The first sentence states the core functionality, the second provides specifics on what is removed and skipped, and the third gives a prerequisite. Every sentence is meaningful and contributes to understanding.
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?
Given the absence of an output schema and annotations, the description is reasonably complete. It covers the tool's purpose, precondition (run scan_dead_code first), safe removal behavior, and dry_run option. It does not explain return values or success confirmation, but for a cleanup tool, the behavior is well-specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already describes both parameters adequately. The description adds no further parameter-level details beyond what is in the schema, resulting in a baseline score of 3.
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 description clearly states the verb 'removes dead code' and the resource (dead code found by scan_dead_code). It implicitly distinguishes from the sibling 'scan_dead_code' by specifying that this tool cleanses while the other scans.
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?
The description provides explicit usage guidance: 'Always run `scan_dead_code` first.' It also indicates what the tool skips (risky elements), helping the agent decide when to use it. However, it does not explicitly state when not to use it or list alternatives beyond the prerequisite.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully bears the burden of disclosure. It clearly states the tool appends to a file (mutation), implying a persistent effect. It does not cover error conditions or permissions, but the core behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, each earning its place: the first explains what and where, the second gives usage timing. No fluff, front-loaded with key information.
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?
For a simple one-param setup tool, the description is sufficient: it explains purpose, target file, and when to run. It does not describe return value (acceptable without output schema) or error handling, but the core completeness is high.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for 'project_root'. The tool description adds no new parameter info beyond the schema; it only restates 'project root' in context. Slight value from connecting parameter to file location, but baseline 3 is appropriate.
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 description clearly states the action (appends rules), target (AGENTS.md or CLAUDE.md in project root), and purpose (enforce rules every session). It distinguishes from siblings like cleanse or scan, which are different operations.
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?
Explicitly says 'Run this once when adding clenzer to a project,' providing a clear when-to-use context. However, it does not explicitly state when not to use or mention alternatives among siblings, though the unique setup role makes it obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses that results are stored in session state—a key behavioral trait. It outlines the scanning scope but omits potential limitations like performance or handling of JavaScript files.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. Front-loaded with the primary function, then storage detail. Highly efficient.
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?
For a tool with 3 params, no output schema, and no annotations, the description covers purpose, stored results, and sibling linkage. Missing guidance on interpreting results beyond use by `cleanse`, but adequate overall.
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 coverage is 100% (all params described). The description adds the default for `include` (all TS/JS files) not present in the schema, enhancing understanding beyond the schema alone.
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 description specifies the tool scans TypeScript/JavaScript projects for dead code types (unused imports, variables, exported functions), distinguishing it from siblings like scan_complexity. The verb 'scans' and resource 'dead code' are clear.
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?
The description states results are stored for use by `cleanse`, implying a preparatory context. It doesn't explicitly state when-not-to-use or alternatives, but the sibling context (e.g., scan_complexity) provides differentiation.
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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- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
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