MergeLoop
OfficialServer Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool in the set, there is no possibility of confusing it with another. The single tool's purpose is clear and distinct simply because it is the only entry point.
Naming Consistency5/5The single tool name follows a clear server_action pattern ('mergeloop_run'). With only one tool, there are no internal inconsistencies or mixed conventions to confuse the naming scheme.
Tool Count3/5One tool for this narrow orchestration workflow is borderline but workable. However, the tool bundles multiple responsibilities (running, capturing, detecting disagreements, bundling) that could benefit from being split into smaller, more granular tools.
Completeness4/5The tool covers the core workflow of running local model CLIs and producing a synthesis-ready bundle. Minor gaps exist around inspecting past runs or customizing the synthesis process, but these are acceptable given the tight scope.
Average 4/5 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
- 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 Apache 2.0.
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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavior. It transparently outlines the operational pipeline: running CLIs, capturing outputs, identifying disagreements, and returning a bundle. However, it does not disclose potential side effects such as spawning external processes, resource consumption, failure modes, or prerequisite setup.
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?
A single, front-loaded sentence captures all essential behavior with no filler. Every phrase contributes a new piece of meaning: execution, mode options, output handling, and return value.
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 moderate complexity of 4 parameters with 2 enums and no output schema, the description sufficiently conveys the tool's purpose and outcome. It mentions the synthesis-ready bundle, which serves as the return-value context, and the schema covers parameter choices. Minor gaps remain around default worker behavior and execution times, but the core invocation is clear.
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 description coverage is 100%, so the description is not required to add parameter-level meaning. It reinforces the mode semantics ('single or council') and the output purpose ('synthesis-ready bundle'), but does not add information beyond what the schema already provides.
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 uses a specific verb ('Run') with a clear resource ('local model CLIs'), and enumerates distinct behaviors: single/council mode, capturing outputs, identifying disagreements, and returning a synthesis-ready bundle. This precisely defines the tool's scope despite the absence of sibling tools to differentiate from.
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 conveys clear usage context by defining the two modes ('single or council') and the intended purpose ('capture outputs, identify disagreements'). There are no explicit exclusions or alternative tools listed, but since no siblings exist, the guidance is clear for the available context.
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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