lpm_llm_context

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

No annotations are provided, so the description bears full responsibility for disclosing behavior. It describes the output content (guide with code, patterns, gotchas) but does not specify any behavioral traits such as network requests, permissions needed, response format, or side effects. This is insufficient for full transparency.

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 concise with two sentences: the first enumerates content, the second states the use case. Every sentence adds value, no redundancy or fluff. Perfectly sized for quick comprehension.

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 no output schema or annotations, the description adequately conveys the tool's purpose and output content. However, it could be improved by mentioning the output format (e.g., plain text or structured markdown) and any prerequisites or constraints for using the guide.

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 covers both parameters with descriptions (name format and version default), achieving 100% schema coverage. The description adds no additional semantic meaning beyond what the schema already provides, justifying the baseline score of 3.

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 retrieves an LLM-optimized usage guide for an LPM package and lists specific content items (purpose, quickStart code, key exports, etc.), distinguishing it from similar tools like lpm_package_context by emphasizing the 'LLM-optimized' nature.

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 includes a clear use case: 'quickly understand how to use a package correctly.' It provides a strong context for when to use, but lacks explicit when-not or mention of alternatives like lpm_package_context, which would improve guidance.

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