code_suggest

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool returns ranked suggestions with relevance scores, which is useful behavioral context. However, it doesn't disclose important traits like whether this is a read-only operation, potential rate limits, authentication requirements, or what happens with invalid inputs. For a tool with no annotation coverage, this leaves significant gaps.

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 perfectly concise with two sentences that each earn their place: the first states the core purpose, the second specifies the return format. It's front-loaded with the main functionality and wastes no words on unnecessary 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?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return format, but doesn't address behavioral aspects, error conditions, or provide usage examples. Without annotations or output schema, more context about the response structure and operational constraints would be helpful.

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 100%, so the schema already fully documents all three parameters. The description doesn't add any parameter semantics beyond what's in the schema - it mentions the general purpose but doesn't provide additional context about parameter usage, constraints, or examples. This meets the baseline for high schema coverage.

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 with specific verb ('Get AI-powered medical code suggestions') and resource ('from a clinical description'), and distinguishes it from siblings by specifying it returns ranked suggestions with relevance scores. This differentiates it from tools like code_lookup or code_validate that likely perform different functions.

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 implies usage context (when you need code suggestions from clinical descriptions) but doesn't explicitly state when to use this vs. alternatives like code_crossref or code_reimbursement. It provides some guidance through the parameter descriptions (e.g., codeType enum values), but lacks explicit when/when-not instructions or named alternatives.

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