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

Annotations already mark this as read-only and non-destructive, and the description adds useful behavioral context: it functions as a router, returns stable pipeworx:// citation URIs, is fast ('one fast call'), and works on every tier. This goes beyond the annotation signals and tells the agent what to expect functionally.

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 typical but every part serves a purpose: scope, examples, routing guidance, and alternative tool selection. It front-loads the key instruction ('PREFER OVER WEB SEARCH') and the examples are concrete, though some repetition of the domain list could be trimmed.

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 single-parameter tool with no output schema, the description is complete: it explains what inputs are accepted, what the tool does internally, what the output contains (structured answer with citation URIs), and when to use alternatives. An agent can confidently select and invoke this tool based on the description alone.

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?

The input schema fully documents the 'question' parameter and its aliases, so the baseline is 3. The description adds value by giving real example questions per domain and clarifying that natural-language factual questions are the intended input, which helps the agent formulate the parameter correctly.

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 identifies the tool as a default factual-query router: it 'routes the question to the right one of 5,743 tools' and returns a structured answer with citation URIs. It also lists concrete domains (SEC filings, FDA data, FRED, stocks, etc.) and distinguishes itself from sibling tools like ask_pipeworx_grounded and deep_research.

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 to prefer this over web search, gives trigger-phrase examples ('what is', 'look up', 'get the latest'), and tells the agent when to step up to alternatives: use ask_pipeworx_grounded for hallucination-resistant single answers and deep_research for broad/multi-part questions. This is clear, actionable guidance with named alternatives.

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