pyp6xer_float_analysis

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

The description builds on annotations (readOnlyHint=true, destructiveHint=false) by detailing that it groups activities into buckets and flags near-critical activities. This adds behavioral context beyond what annotations provide, though it could additionally describe the output format or effect on the state.

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 two sentences with no redundancy. Key actions ('analyze', 'groups', 'flags') are front-loaded, and every word adds value. Excellent conciseness.

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 that an output schema exists (context signal), the description need not explain return values. It covers the main functionality and the reliance on cache_key is partially implied by the schema parameter description. Slight improvement could be mentioning that a loaded file is required, but overall complete.

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?

Input schema has 100% description coverage, so the schema already documents all parameters. The description does not add extra meaning beyond what the schema provides, so baseline 3 is appropriate.

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 it analyzes total float distribution, groups activities into float buckets, and flags near-critical activities. This distinguishes it from siblings like pyp6xer_critical_path (which focuses on critical path) and pyp6xer_schedule_health_check (which is broader).

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 implies usage for float distribution analysis but does not explicitly state when to use this tool versus alternatives like pyp6xer_critical_path or pyp6xer_schedule_health_check. No exclusions or selection criteria are provided.

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