mymedi-ai-mcp-server

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 AI-powered suggestions and ranking with scores, but doesn't disclose important behavioral traits like rate limits, authentication requirements, whether it's read-only or has side effects, error handling, or performance characteristics. The description provides basic functionality but lacks operational context.

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 sentence states the core functionality, and the second sentence describes the output format. There's zero wasted language, and the information is front-loaded with the primary purpose stated immediately.

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 (AI-powered code suggestion with ranking) and lack of both annotations and output schema, the description is minimally adequate. It covers what the tool does and the output format, but doesn't address behavioral aspects, error conditions, or provide enough context for an agent to understand operational constraints. The absence of output schema means the description should ideally explain return values more thoroughly.

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?

With 100% schema description coverage, the schema already documents all three parameters thoroughly. The description doesn't add any parameter semantics beyond what's in the schema - it mentions clinical descriptions and code suggestions generally but provides no additional details about parameter usage, constraints, or interactions that aren't already in the schema descriptions.

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 specific action ('Get AI-powered medical code suggestions') and resource ('from a clinical description'), with explicit mention of the output format ('ranked code suggestions with relevance scores'). It distinguishes this tool from siblings like code_lookup or code_validate by emphasizing AI-powered suggestion generation rather than validation or direct lookup.

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 (converting clinical descriptions to codes) but doesn't explicitly state when to use this tool versus alternatives like code_lookup or code_validate. No guidance is provided about prerequisites, limitations, or scenarios where this tool would be preferred over sibling tools.

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