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

The description goes well beyond the readOnly/idempotent annotations: it discloses authentication requirements, the meaning of 403 errors, the post-2021 collapse in SEC output, that resumption is permission not evidence of quoting, that tickers are NULL in most pre-2022 rows, and that short date windows may legitimately be empty. This gives an agent accurate expectations for edge cases and failure modes.

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 dense yet every sentence adds value: scope, row semantics, bulk-order behavior, data quirks, authentication errors, and empty-result expectation. It is front-loaded with the core definition and then addresses operational caveats. No filler or redundant restatement of the tool name.

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 an 18-parameter, no-output-schema tool, the description is remarkably complete. It tells the agent what each row represents, how to interpret resumption, how to handle bulk orders, when data may be missing, how API errors map to permission problems, and how recent data may lag. The schema covers parameter formats, and the description covers real-world behavior and interpretation.

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?

Input schema coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema: bulk orders share one release_number across many issuers, ticker NULL behavior, the normalized cited-reason taxonomy, and how issuer-level rows relate to order-level data. It still does not enumerate every parameter, but the schema already documents those thoroughly.

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 immediately identifies the resource: every SEC Section 12(k) trading suspension order from 1995 to today. It specifies the data scope, row granularity, and key fields (halts, resumption, reason taxonomy, order PDF URL), making it unmistakable what the tool retrieves. No sibling tool covers SEC suspensions, so the domain itself distinguishes it clearly.

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 clear context for when the tool is useful ('terminal-risk tape for dying OTC and small-cap names'), explains legitimate empty results, and tells the agent how to collapse bulk-order rows via issuer_count/issuer_index. It does not explicitly name an alternative or a when-not-to-use case, but no sibling tool competes with this dataset, so the omission is minor.

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