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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description adds far beyond them: account/tier gating, parallel decomposition across 5,743 tools, gaps[] for unanswered facets ("never invented"), depth-dependent hop behavior, contradiction scans, citation_uri resolvability guarantees, semantic excerpting rather than head-truncation, and 15-90s latency expectations. No contradiction with annotations.

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 but earns its length: prerequisites are front-loaded first, then purpose, then routing guidance, then depth semantics, then return-format details, then latency. Every sentence carries operational meaning. It is not perfectly lean — depth semantics are partly duplicated from the schema's enum description, and the parenthetical-heavy density slows parsing — but for a tool with no output schema and two consequential parameter choices, the size is justified.

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 complex tool with no output schema, the description covers everything an agent needs to call it correctly: prerequisites and auth gating, return packet shape (verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], contradictions[], hop field), parameter semantics, sibling routing, latency expectations, and failure mode (empty gaps[] for non-catalog topics). Nothing essential is missing.

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 coverage is 100%, so baseline is 3. The description adds value on top by defining what depth levels do behaviorally (standard re-angles unanswered gaps; thorough chases leads and adds contradictions) and by clarifying the question parameter accepts broad/multi-part natural language with concrete examples. The enum descriptions in the schema are already rich, so the added value is reinforcement rather than wholesale compensation.

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?

States a specific verb and resource: "Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources... in ONE call." It explicitly distinguishes itself from siblings with "this is NOT open-web search" and contrasts its batch-decomposition behavior with ask_pipeworx's single LLM call. An agent can immediately tell what this tool is for.

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?

Provides explicit when-to-use and when-not-to-use guidance: best for "broad/multi-part questions over structured data," with worked example questions. Routes away for "a single lookup," for "BREAKING or colloquial CURRENT-NEWS" topics, and for non-signed-in users, each naming ask_pipeworx as the alternative. This is model-tier routing guidance with no inference left to the agent.

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