clj-kondo MCP Server
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 has a single, clear purpose that is distinct by default.
Naming Consistency5/5A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The naming follows a clear verb_noun pattern (lint_clojure).
Tool Count2/5A single tool is too few for a server's purpose, as it limits functionality and suggests an incomplete or overly narrow scope. Typically, servers benefit from multiple tools to cover various operations within their domain.
Completeness2/5The server only provides linting, which is a significant gap for a tool named after clj-kondo, as it might be expected to support other related operations like configuration management or analysis. The surface is severely incomplete for a comprehensive Clojure tooling server.
Average 3.9/5 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
This repository is licensed under MIT 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
- 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 of behavioral disclosure. It specifies what gets processed (Clojure/ClojureScript/EDN content) and the tool used, but doesn't describe output format, error handling, performance characteristics, or what constitutes successful linting. The description adds some value but leaves significant behavioral aspects undocumented.
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 a single, efficient sentence that communicates the essential purpose without any wasted words. It's front-loaded with the core functionality and uses precise terminology. Every element of the description earns its place by adding meaningful information.
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, no output schema, and 3 parameters with good schema coverage, the description provides adequate basic context but leaves gaps. It explains what the tool does but doesn't describe what users can expect as results, how to interpret linting output, or any limitations of the clj-kondo approach. For a tool with no structured output documentation, more guidance would be helpful.
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?
With 100% schema description coverage, the baseline is 3. The description adds value by providing context about what 'lint' means in this context and specifying the exact tool being used (clj-kondo), which helps users understand the linting approach and standards being applied beyond what the parameter descriptions provide.
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's purpose with specific verb ('lint') and target resources ('Clojure/ClojureScript/EDN content'), and specifies the implementation method ('using clj-kondo'). It distinguishes itself by naming the exact linter being used, which is helpful even without sibling tools for comparison.
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 provides implied usage context by specifying what types of content can be linted and the tool being used, but offers no explicit guidance on when to use this tool versus alternatives (though no sibling tools exist). It doesn't mention prerequisites, typical workflows, or integration scenarios.
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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