search_case_studies

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

With no annotations provided, the description carries the full burden. It discloses that the search matches across a specific set of fields and that all matching case studies are returned, effectively indicating an unbounded result set. This is useful behavioral context. It does not explicitly state the operation is read-only, but that is inherently obvious for a search tool. The description provides more behavioral detail than a generic search tool and is not misleading.

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 a single, front-loaded sentence that immediately states the action and resource. It efficiently lists the searched fields and concludes with the return behavior. Every word contributes to understanding, with no fluff or repetition of schema details.

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?

The tool is simple (one required parameter, no output schema), and the description covers the input meaning and result scope ('all matching case studies'). It does not describe the structure of a case study object in the response, which could be useful without an output schema, but the overall context is sufficient for an agent to invoke the tool correctly for most search scenarios.

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?

The schema alone defines the 'query' parameter as a search term with 100% coverage. The description adds significant meaning by listing all the content fields against which the query is matched, giving the agent a concrete understanding of what 'match' entails. This goes beyond the schema's generic description, though it does not add syntax or formatting constraints.

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 starts with the specific verb 'Search' and identifies the exact resource ('Contentful case studies (success stories)'). It further disambiguates from sibling tools by enumerating the fields being matched (company name, meta title, meta description, overview, quote, use case, impact, and body content) and explicitly states the return behavior ('Returns all matching case studies'). This is a model of clear, specific purpose.

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 clearly implies when to use this tool: when searching for case studies/success stories, not blog posts or events. It provides a distinct context from sibling tools by naming the content type and fields. However, it does not explicitly mention alternatives or state 'use this instead of X when Y', so it stops short of a 5.

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