gograph_godobj

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with 'Read-only; no side effects.' Beyond that, it adds valuable operational context: the MCP server checks freshness, refreshes in the requested analysis mode, and retries CHA/SSA after source changes for precise graphs. No contradictions with annotations; the extra context enhances transparency.

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 well-structured with clear sections (purpose, behavior, thresholds, when to use, returns). It is longer than the high-reference example but every sentence contributes useful information. Slightly verbose, but the structure and front-loaded purpose make it easy to scan.

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?

The description is complete for a moderately complex analysis tool. It covers purpose, thresholds and qualification logic, freshness/refresh behavior, usage guidelines, alternatives, and return format ('Ranked candidates with method, field, and call counts; empty when no threshold is exceeded'). No output schema exists, so describing returns is necessary and done well.

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 four parameters with descriptions and defaults (coverage 100%). The description goes further by explaining the qualification logic: 'Exceeding any enabled threshold qualifies a struct, and the combined excess determines rank.' It also restates defaults and clarifies that top limits results, adding semantic meaning beyond the schema's simple 'Minimum ...' labels.

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 and resource: 'Detect God Object anti-pattern candidates by scoring structs on method count, field count, and outgoing call count.' This clearly states what the tool does and highlights the unique scoring criteria, distinguishing it from sibling tools. The NOT TO USE section further clarifies its scope by contrasting with gograph_fields and gograph_complexity.

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?

WHEN TO USE is explicitly stated: 'During architecture reviews to find monolithic structs that should be decomposed.' NOT TO USE provides concrete alternatives: 'For general struct layout inspection (use gograph_fields); for single-function complexity (use gograph_complexity).' This is an exemplary model of when/when-not guidance.

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