LLMScout
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Only one tool exists, so there is no possibility of confusing it with others. The description clearly states the tool runs the LLMScout CLI with init/check/fleet subcommands, making its purpose distinct and unambiguous.
Naming Consistency5/5The single tool name 'run' is a clear verb that directly reflects its action. Since there is only one tool, naming consistency is inherently maintained with no conflicting conventions.
Tool Count3/5The server exposes only one tool, which feels thin for a typical MCP server. While the CLI covers multiple subcommands, a richer set of individual tools might better match the expected scope and provide finer-grained control.
Completeness5/5The tool description indicates it can run all the essential CLI subcommands (init/check/fleet), so the core functionality is covered. There are no obvious gaps in the agent's ability to perform the intended operations through this single tool.
Average 3.3/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 60 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only mentions the high-level action of running a CLI and provides example subcommands. It does not disclose potential side effects, permissions, output behavior, or any cautions. With no annotations, the description carries the full transparency burden and is insufficient.
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 a single, clear sentence that is front-loaded with the verb and resource. It is concise and free of unnecessary words, though it could be more informative within the same length.
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?
Running a CLI can be complex, but the description only lists three subcommands without explaining the purpose of llmscout or how output is handled. The output schema provides some coverage, but the description alone is not complete enough to fully guide an agent.
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?
The description offers example subcommands (init/check/fleet) that hint at valid values for the args array, but it does not explain how args should be structured or what additional arguments/flags are supported. With 0% schema description coverage, this partial guidance is helpful but incomplete.
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 that the tool runs the llmscout CLI and specifies the subcommands (init/check/fleet). This identifies the exact resource and action, and even though there are no siblings, it is specific enough to stand alone.
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?
No explicit when-to-use or when-not-to-use guidance is provided. The only hint is that this tool runs a specific CLI, implying usage when llmscout commands need to be executed, but alternatives or exclusions are not mentioned.
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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