list_emulated_apis

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

No annotations provided, so description must fully disclose behavior. It explains the default output is not the full list but a count and sample categories, and mentions the list is large. This is sufficient for understanding the tool's behavior.

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?

Two well-structured sentences. First states purpose, second provides usage guidance and output details. No filler.

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 description covers purpose, usage, and default output. It does not explicitly describe the output format or whether a full list can be obtained, but for a simple listing tool with no parameters, this is largely complete.

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?

No parameters in schema, but description adds crucial context about default output behavior (count and sample categories) and scale (thousands of APIs). This goes beyond the schema.

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 it returns the list of Win32 APIs Speakeasy can emulate, using a specific verb ('Return') and resource ('list of Win32 APIs'). It differentiates from siblings by positioning itself as a pre-check before emulation, unlike 'emulate_binary' which actually emulates, and 'check_speakeasy' which is likely a different check.

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?

Explicitly states when to use: 'Useful for "can Speakeasy handle this binary's API surface?" before calling emulate_binary on a long-running target.' This provides clear context and alternatives.

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