UAAR University MCP Server

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

Annotations indicate the tool is read-only, non-destructive, and idempotent, which the description doesn't contradict. However, the description adds minimal behavioral context beyond the annotations, such as the input format example, but fails to explain rate limits, error handling, or output format. With annotations covering safety, a baseline score is appropriate.

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 brief and to the point, consisting of two sentences that directly state the purpose and provide an input example. It avoids unnecessary fluff, making it efficient, though it could be slightly more structured by separating usage notes from the example.

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 (single parameter with nested objects), lack of output schema, and rich annotations, the description is minimally adequate. It covers the basic function and input format but misses details like output structure, error cases, or integration with sibling tools, leaving gaps for an AI agent to infer.

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 input schema has 0% description coverage, providing no details about the 'grades' parameter. The description adds an example structure ([{'credit_hours': 3, 'grade': 'A'}, ...]), which clarifies the expected format and key fields, partially compensating for the schema gap. However, it doesn't fully document all aspects, such as valid grade values or credit hour constraints.

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: 'Calculate GPA from a list of grades.' It specifies the verb ('Calculate') and resource ('GPA'), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'get_cgpa', which might serve a similar purpose, preventing a perfect score.

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 'get_cgpa' or other academic tools in the sibling list. It lacks context about prerequisites, such as whether grades need to be in a specific format or system, and offers no exclusions or comparisons to other tools.

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