UAAR University MCP Server

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

Annotations cover key traits: read-only, non-destructive, idempotent, and closed-world. The description adds context by specifying the optional filtering capability, which isn't in annotations. However, it doesn't disclose additional behaviors like rate limits, error handling, or what 'contact information' entails (e.g., phone, email). No contradiction with annotations exists.

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 two sentences, front-loaded with the core purpose and followed by the filtering detail. Every word earns its place with no redundancy or fluff, making it highly efficient and easy to parse for an AI agent.

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 low complexity (one optional parameter), rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return values), the description is mostly complete. It covers the purpose and parameter use but lacks guidance on when to use versus siblings, which is a minor gap.

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%, so the description must compensate. It adds meaning by explaining that the parameter 'department_name' is for optional filtering, clarifying its purpose beyond the schema's title 'Department Name.' With only one parameter, this is sufficient, but it doesn't detail format constraints (e.g., exact names vs. partial matches).

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: 'Get contact information for university departments.' It specifies the verb ('Get') and resource ('contact information for university departments'), making the function unambiguous. However, it doesn't explicitly differentiate from siblings like 'list_departments' or 'get_university_info,' which could provide overlapping information.

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 minimal guidance: 'Optionally filter by name.' It implies usage for retrieving department contacts but offers no explicit when-to-use advice, such as when to choose this over 'list_departments' for contact details versus general listings, or prerequisites like authentication needs. There's no mention of alternatives or exclusions.

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