trace-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the types of metrics returned (coupling, cycles, etc.), which gives some insight into output behavior, but fails to describe critical aspects like whether this is a read-only operation, performance characteristics, data freshness, or error conditions. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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 extremely concise and front-loaded, consisting of just two brief phrases that efficiently convey the tool's purpose and primary use case. Every word earns its place with no redundancy or unnecessary elaboration, making it easy to parse quickly.

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?

Given the tool has 0 parameters and no output schema, the description provides adequate basic information about what the tool does and its intended use. However, without annotations and with no output schema, it should ideally provide more behavioral context (e.g., what format the health metrics come in, whether it's a snapshot or trend analysis). The description is minimally complete but could be more helpful for an agent invoking the tool.

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?

The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the input requirements. The description doesn't need to add parameter information, and it appropriately doesn't mention any parameters. This meets the baseline expectation for parameterless tools, though it doesn't provide extra context about implicit inputs (like project context).

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 purpose: analyzing structural health metrics like coupling instability, dependency cycles, PageRank rankings, and refactor candidates. It specifies the verb 'use for architecture review' and the resource 'structural health', making it distinct from many sibling tools that focus on different aspects (e.g., get_call_graph, get_complexity_report). However, it doesn't explicitly differentiate from similar tools like get_coupling or get_refactor_candidates, which slightly limits sibling distinction.

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 implied usage context with 'Use for architecture review', suggesting it's intended for high-level project assessment. However, it lacks explicit guidance on when to choose this tool over alternatives (e.g., vs. get_health_trends or get_coupling_trend), and doesn't mention prerequisites or exclusions. The guidance is useful but incomplete for optimal tool selection.

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