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

With no annotations, the description carries the burden. It mentions data sources (field studies, podcast numbers) and covers specific topics, but does not disclose return format, potential empty results, or performance. It gives some context but not rich behavioral detail. Since it's a read-only 'get' operation, the safety is implied, but it could state that it returns a single number or range.

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 three sentences, front-loaded with the core purpose, and provides essential context without unnecessary details. Every sentence adds value.

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 one simple parameter and no output schema, the description is nearly complete. It explains what data is available due to the study scope, how to use it, and when to use it. It could specify the return format, but given the simplicity, this is adequate.

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 describes the 'topic' parameter with examples, so baseline is 3. The tool description adds specific example topics (RevOps headcount ratio, CRM adoption, time to Series B) and explains what kind of benchmark data is covered (field studies, podcast), providing richer semantics beyond the schema's generic description.

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 verb 'Get' and the resource 'proprietary LeanScale benchmark data on a topic,' and explicitly lists the domains covered (GTM tech stacks, AI adoption, RevOps ratios, etc.). It distinguishes itself from siblings like get_case_study and get_playbook by focusing on benchmark data rather than narratives or playbooks.

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 includes explicit usage guidance: 'Use when someone asks what's normal, what do other companies do, or wants a number to compare against.' It does not explicitly state when not to use or name alternative tools, but the context is clear enough to guide selection among siblings.

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