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

Goes well beyond the readOnly/openWorld/idempotent annotations: it discloses the full output contract (findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation), the honesty guarantee (explicit gaps[] for unanswered facets, never invented), per-depth hop behavior, latency expectations (15-60s, up to ~90s for thorough), and the citation_uri fetchability guarantee. No contradiction with annotations — the 'NOT open-web search' phrasing clarifies source type, not world-sensitivity, and fetched_at timestamps reinforce the open-world nature.

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 critical decision factors are front-loaded (account requirement, fallback routing, 'NOT open-web search'), and nearly every clause carries operational value for a tool this complex. However, it is one dense run-on paragraph, the depth/hop semantics are partially restated from the schema, and there are redundant asides, so it is not as tight or scannable as it could be.

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 high-complexity tool with no output schema, the description carries the full burden of explaining return values — and it does: packet fields, gaps[], contradictions[], hop field, citation_uri semantics, and semantic excerpting of large records. Auth prerequisites, fallback routing, source scope, depth behavior, and latency are all covered. Only minor details like the confidence scale are absent, which does not hinder correct invocation.

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 the baseline is 3; the schema already documents depth's enum values (facet counts, hops, contradictions[]) and question's natural-language semantics in detail. The description adds latency expectations per depth and reinforces the paid-plan gating of 'thorough,' providing expectation-setting context beyond the schema, though it partly duplicates the depth enum descriptions.

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+resource: grounded multi-source research across 1,517 STRUCTURED data sources (SEC, FRED/BLS, FDA, USPTO, markets, science, government). It sharply distinguishes itself with 'this is NOT open-web search' and gives concrete example queries, so an agent can immediately tell it apart from siblings like validate_claim, bet_research, or ask_pipeworx.

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?

Explicitly states when to use it ('best for broad/multi-part questions over structured data'), when not to ('For a single lookup use ask_pipeworx', 'this is NOT open-web search'), and names the fallback alternative with a concrete condition ('If you are not signed in, use ask_pipeworx instead — it works on every tier'). Depth-tier gating (thorough needs a paid plan) is also disclosed up front.

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