gograph_complexity

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

Annotations already declare read-only and non-destructive, but the description adds substantial behavior: UNKNOWN handling with score -1, freshness checks and retry logic in specific graph modes, and side-effect-free guarantee. This goes well beyond the structured annotations.

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 longer than the ideal two-sentence pattern but is well structured with labeled sections (WHEN TO USE, NOT TO USE, RETURNS). It remains readable and every sentence contributes useful 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?

Even without an output schema, the description covers return shape (list of functions with score and severity label), empty-result behavior, and edge cases (UNKNOWN functions). This is complete for a reporting tool with no nested objects.

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% for the only parameter `symbol`, so baseline is 3. The description merely restates that it is an optional substring filter, adding minimal semantic value beyond the 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 opens with a specific verb+resource: 'Report estimated cyclomatic complexity for Go functions' and adds sorting and severity labels. It clearly distinguishes from sibling tools by naming alternatives like gograph_coupling and gograph_deps.

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?

Explicit 'WHEN TO USE' and 'NOT TO USE' sections are present, with concrete alternatives for other metric types. This gives the agent unambiguous guidance on tool selection.

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