graph_frequency

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

With no annotations, the description must disclose behavioral traits. It indicates the tool analyzes frequency and identifies patterns, implying a read-only query. However, it does not explicitly state whether it modifies any state, requires special permissions, or has performance impacts, leaving room for ambiguity.

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 three sentences, each adding value: action, outcomes, and use case. It is front-loaded with the core purpose and contains no redundant information.

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?

Despite lacking an output schema, the description conceptually outlines what the tool returns (hot/cold graphs, saturation) and ties it to a practical use case. It provides sufficient context for understanding the tool's role, though it could benefit from mentioning the return format or data structure.

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 coverage is 100%, so the description adds no additional meaning beyond the schema. It does not mention any parameters or explain their semantics beyond what is already in the input schema.

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 the tool's function with a specific verb ('Analyze') and resource ('CUDA Graph launch frequency per executable'). It further elaborates on sub-analyses (hot/cold graphs, saturation) and distinguishes from sibling tools like 'graph_lifecycle' which likely focuses on lifecycle events.

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 provides a concrete use case ('Essential for vLLM batch size tuning') but lacks explicit guidance on when to use this tool over siblings like 'get_trace_stats' or 'graph_lifecycle'. It does not mention when not to use it or alternative tools for similar tasks.

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