agent-eval
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool available, there is no possibility of confusing it with another; its purpose is clearly described.
Naming Consistency4/5The tool name 'run' is a simple, direct verb that matches its function. While it lacks a noun modifier, there is no inconsistency to penalize.
Tool Count3/5The server has a single tool, which feels thin for a general evaluation platform but is appropriate for a focused CLI wrapper. It is borderline but not excessive.
Completeness4/5The server's stated purpose is to run the agent-regress CLI, and the single tool fulfills that. However, it lacks auxiliary capabilities like listing available tests or parsing results separately.
Average 3.6/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 120 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 failing
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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?
With no annotations, the description must carry the full burden of behavioral disclosure. It mentions that the tool returns parsed --json output, which is useful, but it does not disclose potential side effects of running arbitrary CLI arguments, such as file modifications or system changes. For a generic runner, this lack of safety information is a significant gap.
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 short and front-loaded with the main action. However, the quoted 'Real CLI --help output' repeats the same purpose statement, making it slightly redundant. Overall, it is efficient and well-structured, though a single sentence would have sufficed.
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 that this is a simple wrapper with one parameter and an output schema, the description is reasonably complete for basic use. However, it does not provide examples of typical arguments or explain error handling/exit codes, which would be valuable for a CLI runner. The output schema likely covers return values, but the description could still offer more context.
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 input schema has one parameter 'args' with 0% description coverage, so the description must compensate. It says 'with the given arguments,' which clarifies that the array items are CLI arguments, but it does not elaborate on format, ordering, or examples. This adds minimal meaning beyond the schema but is not completely unhelpful.
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 runs the agent-regress CLI with given arguments and returns parsed JSON output. It is specific about the resource (agent-regress CLI) and the action (run), and mentions statistical regression testing for LLM agents, leaving no ambiguity about its 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?
The description provides clear context by identifying the tool as a regression testing utility for LLM agents, implying when it should be used. Although there are no sibling tools to compare against, the context is specific enough to guide an agent. It doesn't explicitly exclude alternatives but does not need to, as no alternatives 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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- Evaluate tool definition quality.
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