analyze

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

With no annotations, the description discloses the output (detailed report on latency, cost, throughput, reliability, and fixes), but does not clarify whether the tool has side effects, requires specific permissions, or is strictly read-only. The mention of 'actionable fixes' could imply code modification, but it remains ambiguous.

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?

The description is two sentences, front-loaded with purpose and output. Every word earns its place, and there is no redundancy or filler.

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 tool with no output schema, the description provides a high-level return summary but lacks detail on report shape, whether files are modified, or how results are structured. Given its complexity (4 params) and zero output schema, it is complete enough for tool selection but leaves gaps for invocation expectations.

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 covers all parameters with descriptions (100% coverage), so baseline is 3. The description adds extra semantic value by framing the output dimensions (latency, cost, etc.) and the engine, which enriches understanding of the 'fixes' and 'benchmark' parameters, pushing it above baseline.

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 a specific verb ('Analyze') and resource ('code for LLM inference issues using PeakInfer engine'), and distinguishes itself from sibling tools like get_helicone_events or get_inferencemax_benchmark by focusing on code analysis rather than trace retrieval or benchmark comparisons.

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?

The description implies usage for code-level inference analysis, but provides no explicit guidance on when to choose this tool over siblings or when not to use it. It lacks exclusions or alternative mentions, so the context is only implied.

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