mymedi-ai-mcp-server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the output format (approval likelihood, confidence, etc.) but lacks critical behavioral details such as whether this is a read-only operation, if it requires specific permissions, rate limits, or how it handles errors. The description is functional but incomplete for safe agent use.

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, well-structured sentence that efficiently conveys the tool's purpose and output. It front-loads the core function and lists return values without unnecessary elaboration, making it easy for an agent to parse quickly.

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 complexity of a prediction tool with 5 parameters and no output schema, the description is partially complete. It specifies the output format but lacks details on behavioral traits, error handling, and usage context. With no annotations to supplement, it falls short of being fully informative for reliable agent invocation.

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 description coverage is 80%, providing a solid baseline. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain relationships between parameters (e.g., how diagnosis codes affect prediction) or usage nuances. This meets the minimum viable baseline given the 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 verbs ('predict prior authorization approval probability') and resources ('for a procedure'), and it distinguishes from siblings by focusing on PA prediction rather than validation, lookup, or other healthcare functions. The detailed output specification further clarifies its unique role.

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 like 'pa_status' or other sibling tools. It mentions what the tool does but offers no context about prerequisites, typical use cases, or comparisons to similar tools in the server.

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