generate_code

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('generate') but doesn't explain what this entails—e.g., whether it's a read-only operation, if it requires specific permissions, what the output format is, or any rate limits. The description is too vague to inform the agent about behavioral traits beyond the basic purpose.

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, efficient sentence: 'Generate API call code from a captured request.' It's front-loaded with the core purpose and contains no wasted words, making it highly concise and well-structured for quick understanding.

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?

Given the tool's moderate complexity (3 parameters, no annotations, but with an output schema), the description is incomplete. It states the purpose but lacks usage guidelines, parameter semantics, and behavioral context. The presence of an output schema means return values are documented elsewhere, but the description doesn't provide enough context for effective tool invocation without additional information.

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 0%, meaning parameters are undocumented in the schema. The description adds no semantic details about parameters like 'request_id', 'language', or 'framework'—it doesn't explain what a 'request_id' refers to, what languages are supported, or how the framework influences code generation. This fails to compensate for the low coverage, leaving parameters largely unexplained.

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: 'Generate API call code from a captured request.' It specifies the verb ('generate'), resource ('API call code'), and source ('captured request'), making the function unambiguous. However, it doesn't distinguish this tool from siblings like 'analyze_api' or 'export_websocket_session_raw', which might also involve code or request handling, so it misses full differentiation.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a captured request via tools like 'get_request' or 'list_requests'), exclusions, or comparisons to siblings such as 'analyze_api' for analysis instead of code generation. This leaves the agent with minimal context for tool selection.

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