list_ai_traces

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that the tool lists traces (read operation) and that data comes from instrumented AI apps, but does not mention any specific behaviors like pagination, rate limits, or what happens with empty results. Adequate for a simple list operation but could add more detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the main purpose, no filler. Every sentence adds value: purpose, scope clarification, and alternative tools. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple list tool with full parameter schema coverage and no output schema, the description is complete. It explains what the tool does, its data source, and how it relates to sibling tools. No obvious gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents the three parameters. The description mentions parameters implicitly ('hours', 'limit', 'service') but does not add new meaning beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it lists recent AI traces from OpenTelemetry, specifies what it includes (LLM calls, cache lookups, etc.), and distinguishes itself from sibling tools by noting it is not tied to a Valkey instance.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use alternatives: 'Use get_ai_trace for a full span waterfall and correlate_ai_trace to join a trace with live Valkey state.' Also clarifies scope by stating it is not tied to a Valkey instance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.