Scalene-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 tool analyzes data and describes the return structure, but lacks critical details such as whether it's read-only or mutative, performance implications, error handling, or rate limits. For a tool with 6 parameters and no annotations, this is a significant gap.

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 appropriately sized and front-loaded with the core purpose. The parameter explanations are necessary given the 0% schema coverage, but the structure could be slightly more streamlined (e.g., integrating parameter details more seamlessly). Overall, most sentences earn their place.

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 complexity (6 parameters, 0% schema coverage) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers parameter semantics thoroughly and hints at output variability. However, it lacks behavioral context and usage guidelines, which are important for a tool with multiple analysis types.

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 description coverage is 0%, so the description must compensate fully. It provides detailed semantics for all 6 parameters: explains 'profile_id' source, lists 'metric_type' options, clarifies 'top_n' purpose, defines thresholds for bottlenecks, and specifies 'filename' requirement for file analysis. This adds substantial meaning beyond the bare 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 'analyzes profiling data with flexible analysis types,' which is a specific verb+resource combination. It distinguishes itself from siblings like 'list_profiles' or 'profile' by focusing on analysis rather than listing or creating profiles. However, it doesn't explicitly differentiate from 'compare_profiles,' which might also involve analysis.

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 no guidance on when to use this tool versus alternatives like 'compare_profiles' or other siblings. It mentions 'flexible analysis types' but doesn't specify contexts or prerequisites for usage, leaving the agent to infer based on parameter details alone.

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