ypollak2/llm-router

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

With no annotations provided, the description carries the full burden. It successfully discloses the external dependency ('routes to Perplexity') and data source ('web-grounded'), but omits operational details critical for an external API call: rate limits, authentication requirements, failure modes, or caching behavior.

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 efficiently structured with clear sections: mechanism, use cases, and argument definitions. No sentences are wasted; the information density is appropriate for quick agent parsing.

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?

For a 4-parameter tool with no annotations but an existing output schema, the description adequately covers purpose, usage contexts, and parameter meanings. Minor gap: lacks mention of prerequisites (e.g., Perplexity API configuration) or behavioral constraints.

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?

Given 0% schema description coverage, the description fully compensates by documenting all 4 parameters with clear semantics ('The research question', 'Optional system instructions', etc.). It loses a point for somewhat tautological definitions (e.g., 'system_prompt: Optional system instructions') and lack of constraint details (formats, defaults).

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 explicitly defines the tool as 'Search-augmented research query' that 'routes to Perplexity for web-grounded answers', providing a specific mechanism (Perplexity routing) and resource type (web data) that clearly differentiates it from generic siblings like llm_generate or llm_query.

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 'Best for:' list explicitly identifies appropriate use cases (fact-checking, current events, finding sources, market research), providing clear positive guidance. However, it lacks explicit negative guidance (when not to use) or named alternatives (e.g., 'use llm_generate for creative writing instead').

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