audit_context_packing

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

No annotations provided. Description discloses that findings cover content inventory, redundancy, freshness, ordering, truncation risk, and prompt-cache utilization. Also notes that literal dumps produce sharper findings. This is sufficient behavioral disclosure.

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?

Two sentences, directly stating purpose, usage, and output. No extraneous information. Well structured with key information front-loaded.

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?

No output schema present, but description explains the coverage of findings (6 aspects). Adequate for a tool that returns a simple text analysis. Could be improved by mentioning number of findings or format, but current is sufficient.

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?

Both parameters have descriptions in the schema. The description adds extra meaning by explaining what kinds of input are acceptable (literal or structured) and notes that 'literal dumps produce sharper findings,' which goes 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 states it analyzes context payload for efficiency, redundancy, and ordering. It names specific outputs (top 3 findings) and distinguishes from sibling audit tools by focusing on context packing.

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?

Explicitly says 'Use this when a user is concerned about agent cost, latency, or quality degradation on long conversations.' It also clarifies acceptable input formats. No explicit exclusion criteria, but the context is clear.

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