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

Beyond the readOnlyHint, the description adds valuable behavioral traits: returns up to 10 relevance-ranked recommendations, includes two content types, mandates that URLs must be included as clickable links, and notes that topic-filtered responses list related_topics. It also explains content_id usage for get_content_details. This is rich behavioral disclosure that goes well beyond the annotation.

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?

Although the description is relatively long, every sentence adds critical operational detail. It is well-paced: starts with purpose, then usage, then result behavior, then filters, and ends with search prerequisites. The structure is logical and fully utilizes the word budget without redundancy.

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 search tool with six optional parameters and no output schema, the description covers all necessary aspects: input constraints, result format, link requirements, related_tools, and filter behaviors. It is complete enough for an agent to invoke the tool correctly in a wide variety of user queries.

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%, but the description adds significant operational semantics: topics intersection behavior, author exact-match requirement, filetype rules of thumb (e.g., tier1_consulting_deck = McKinsey-style), and default content_type behavior. These details are not in the schema descriptions and help the agent choose and combine parameters correctly.

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 clearly states the tool's purpose: "Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies." It uses a specific verb and resource, and distinguishes itself from siblings by mentioning content_id for get_content_details and related_topics. This is a clear, non-tautological purpose statement.

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 provides explicit when-to-use guidance: "Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example..." It also gives operational rules such as "Provide at least one of query, topic(s), or author" and points to list_topics for topic mapping. While it doesn't mention all siblings, it clearly frames when this search tool is appropriate versus specialized follow-up tools.

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