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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds substantial behavioral context beyond that: account requirements and paid tier limitations, the multi-hopped depth behavior, the structure of the findings packet (verbatim evidence, confidence, source, fetched_at, pipeworx:// citation), the gaps[] and contradictions[] fields, semantic excerpting behavior, and expected latency. There is no contradiction with the annotations; the description thoroughly discloses what happens when the tool runs.

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 long and dense, but nearly every sentence earns its place given the tool's complexity: account requirements, alternatives, decomposition behavior, citation semantics, gaps/contradictions, and timing are all relevant operational details. It loses a point because it is a continuous wall of text without any structural segmentation (bullets or paragraph breaks), and some details like '5,743 tools' are colorful but not essential. Still, it is far from bloated or tautological.

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?

With no output schema present, the description carries the full burden of explaining the return value, and it does: 'findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation... explicit gaps[]', plus contradictions[], hop field, and fetchable citation_uri. It also covers latency, account prerequisites, and the difference from ask_pipeworx. An agent has enough information to invoke the tool and interpret its result without other documentation.

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%, so the baseline is 3. The description adds meaningful value beyond the schema by explaining depth options in behavioral terms ('quick=3 single hop', 'standard=3... adds a gap-recovery hop', 'thorough=6... chases leads'), giving example questions, and clarifying that the question parameter can be 'broad/multi-part'. This is above baseline but not necessary to the max because the schema already documents the enum values and question semantics well.

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 states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources' in one call, and explicitly distinguishes itself from 'open-web search' and from siblings like ask_pipeworx. It clearly identifies the tool's behavior: decomposing questions, routing to 5,743 tools, and returning a findings packet. This is more than sufficient for an agent to understand what the tool does and how it differs from nearby tools.

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 guidance: 'Best for broad/multi-part questions over structured data', and gives direct alternatives with conditions: 'For a single lookup use ask_pipeworx', 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx', and 'If you are not signed in, use ask_pipeworx instead'. It even explains why deep_research may return empty gaps for non-catalog topics, which is valuable for tool selection.

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