datasets_techstack_search

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

With no annotations provided, the description carries the transparency burden. It discloses the dataset's index nature, the reverse-index filter model, and the exact-match technology semantics, which sets clear expectations for how the search behaves. It does not mention pagination limits or result shape, but the schema covers constraints like page_size limits, and the search's read-only nature is reasonably implied by 'Search'.

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 but every sentence earns its place: it covers dataset identity, record structure, filter semantics, an example, and enums in a compact form. It front-loads the core action before diving into details. There is minor redundancy with schema enum values, but it does not materially bloat the text.

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 tool with 21 parameters and no output schema, the description covers the central usage patterns thoroughly: dataset identity, filter combination, and the sort/fetch-tier enums. It falls slightly short on explaining output format and pagination behavior, but the schema documents pagination constraints and the search semantics are well conveyed. Overall, an agent can select and invoke this tool correctly with high confidence.

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 by illustrating how `technology`, `any_of`, and `not` combine (AND/OR/exclude) with a concrete example, which is more accessible than the schema's individual parameter descriptions. It also surfaces the `techstack` dataset enum value and restates the sort/render_tier enums, reinforcing understanding.

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 opens with a clear verb and resource: 'Search the website tech-stack dataset.' It further specifies the dataset id (`techstack`), the one-record-per-site structure, and the BuiltWith/Wappalyzer-style scope, making it unequivocally distinct from the many sibling search tools and from the related `datasets_techstack_facets` and `datasets_techstack_item` 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 explicitly explains the core filter usage: repeat `technology` for AND, `any_of` for OR, and `not` for exclusion, with a concrete example (Shopify and Klaviyo but not Recharge). It also enumerates the sort and render_tier enums. It does not explicitly contrast with `datasets_techstack_facets` or `datasets_techstack_item`, but the usage context for this search tool is clear.

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