agent-observability
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Only one tool exists, so there is no possibility of overlap or confusion between tools. The single 'run' tool clearly delegates to CLI subcommands, making its purpose unambiguous.
Naming Consistency4/5With only one tool, naming consistency is trivially maintained. The name 'run' is a simple verb that matches its role as an executor, though it lacks a noun prefix that might better indicate the subject.
Tool Count3/5A single tool for wrapping an entire CLI is borderline. It feels thin for a server that could expose multiple distinct operations, but it is also a pragmatic design if the CLI is the core interface. The count is acceptable but not expansive.
Completeness4/5The single 'run' tool provides access to all JSON-supporting subcommands (list, replay, inspect, diff, run), covering the main workflows of the agent-observability domain. Minor gaps exist, such as no dedicated help or non-JSON commands, but these are not critical for typical usage.
Average 3.7/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 159 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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and discloses important behavior: --json is appended automatically and the output is machine-readable JSON. It also warns that only certain subcommands support --json, which sets expectations about possible failures. It does not cover error handling or exit codes, but covers the most relevant automation behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is mostly front-loaded and informative, but it includes a quoted help line that repeats the first sentence ('Run the agent-trace CLI') and adds no new value. The useful subcommand list earns its place, but the redundant 'Real --help output' section could be removed.
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?
For a simple CLI-runner tool with no output schema, the description adequately explains the main behavior and the --json caveat. However, it does not describe the shape of the returned JSON, error behavior, or how to pass complex arguments, leaving some ambiguity for an agent needing to parse the result.
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 0% because the sole parameter 'args' has no schema description. The description partially compensates by mentioning 'subcommand/args' and listing valid subcommands, implying the array should contain a subcommand followed by arguments. However, it does not specify ordering, flags, or examples, so the compensation is incomplete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs the agent-trace CLI with a given subcommand and args, which is a specific verb+resource. It does not distinguish from siblings because none are listed, but the purpose is unambiguous despite some redundant repetition from the embedded help text.
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 guidance on which subcommands support the automatically appended --json flag (list, replay, inspect, diff, run), helping the agent avoid unsupported invocations. It does not mention alternatives because there are no sibling tools, but the usage context is clear.
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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