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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, and the description still adds real behavioral context: only excerpts are returned ("the bold statement and the start of each section"), fields appear only "where the case study states them", and "nothing is added that the case studies don't publish" — an explicit no-fabrication guarantee. It doesn't cover failure modes for an unmatched slug or what the interactive surface does on selection.

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?

Front-loaded with what the tool produces, then input modes, then example utterances — a sensible order. The middle section is a dense colon-chained run-on that takes a second pass to parse, though almost every clause carries distinct information.

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 an output schema and full annotation coverage, the description's job is mainly input modes and fidelity limits, both well covered. Minor gap: no statement of what happens when a named case study isn't found or when fewer than two are supplied.

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, but the description goes further: it explains that `case_studies` accepts a slug, a URL, or title words (broader than the schema's bare string array) and that `topic` is a mutually exclusive alternative that returns "the closest three" rather than exact matches. Omission semantics ("leave out for the list") are also stated.

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?

Names a specific verb+resource (compare two or three published case studies side by side) and enumerates exactly what gets returned: challenge, strategy, solution, results, client, sector, stack, and per-section links. No sibling tool does case-study comparison, so an agent can route here unambiguously from the name plus this sentence.

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?

Covers all three input modes explicitly — slugs/URLs, a topic for the closest three, or neither to get the choosable list — and supplies trigger phrasing ("compare your case studies", "show me your Databricks projects side by side"). It stops short of naming when NOT to use it (e.g., vs. `search` or `explore_post` for single-item lookups).

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