pack_context

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

Annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and the description confirms 'Read-only'. It adds behavioral details like strategies ('most_relevant', 'core_first', 'compact'), compression, and output format. No contradictions.

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 informative and front-loaded with the main purpose. Each sentence adds value, though it could be slightly more concise by avoiding some redundancy. Still, it efficiently conveys essential details.

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 9 parameters with full schema descriptions, no output schema, and annotations covering safety, the description sufficiently explains use cases, strategies, and output format. It could mention more about the output structure but is adequate for an AI agent.

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 coverage is 100%, but the description adds value by explaining the strategies in detail (e.g., core_first = PageRank always wins) and the effect of compress. Some parameters (like format, include) are not elaborated beyond schema, but overall it enhances understanding.

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 packs project context into a single document for external LLMs, using intelligent selection by graph importance and token budget. It compares itself to Repomix and lists specific strategies, making it distinct from siblings that retrieve specific context pieces.

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 explicitly says 'Use when sharing project context with external tools' and provides a comparison to Repomix. However, it does not explicitly list alternatives among the many sibling tools or state when not to use it, which would enhance guidance.

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