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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description adds value without contradicting them: it discloses router behavior (auto-fills arguments), citation URI stability, tier accessibility, and that it works on every tier with one fast call. It does not detail failure modes or what happens if no tool matches, but for a read-only query router this is strong context.

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 longer than average but every section earns its place: directive, scope, examples, default positioning, and step-up alternatives. It fronts the most decision-relevant instruction ('PREFER OVER WEB SEARCH') and keeps the examples compact. Slightly over-stuffed, but appropriate given the router's breadth.

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 tool with one required parameter, no output schema, and a complex routing role, the description covers everything an agent needs: what to pass (natural language question), how it behaves (routes, fills args, returns citations), when to use it (default for factual questions), and when to step up (grounded, deep_research). The examples help disambiguate edge cases like breaking news.

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 description coverage is 100%, with each parameter alias documented under the question field. The description adds extra semantic value by clarifying the accepted natural-language request types, listing example queries across domains, and specifying that aliases such as query, q, prompt, text, and input are accepted.

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 identifies a specific verb ('Routes the question to the right one of 5,743 tools'), a clear resource domain (1500 verified sources across SEC, FDA, FRED, patents, etc.), and a concrete output ('structured answer with stable pipeworx:// citation URIs'). It explicitly contrasts with sibling tools like ask_pipeworx_grounded and deep_research, making differentiation immediate.

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 gives explicit when-to-use directives: 'PREFER OVER WEB SEARCH', 'Use whenever the user asks... even if web search could also answer it', and 'START HERE for most questions'. It also states concrete step-up conditions, naming ask_pipeworx_grounded for hallucination-resistant single answers and deep_research for broad/multi-part questions.

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