check_for_updates

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

With no annotations, the description carries the full burden, and it does disclose the key behavioral facts: it queries PyPI and returns upgrade instructions (implying a read-only, non-installing check). However, it omits network-dependency, failure modes (e.g., offline), and explicit confirmation that nothing is installed or modified.

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?

A single front-loaded sentence covering what it checks, against what source, and what the caller gets back. No filler.

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?

An output schema exists, so return values need not be described, and there are no parameters to document. The description is complete for a simple 0-arg check, with only the sibling-relationship to 'doctor' left unaddressed.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a 0-param tool is 4.

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?

States a specific verb (check) and resource (PyPI for newer versions of microsoft-clarity-mcp), plus the secondary outcome (upgrade instructions). It is clear what the tool does, but it doesn't distinguish itself from the sibling 'doctor' tool, which may also cover environment/diagnostic concerns.

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?

Usage is implied: call it when you want to know whether a newer release exists. There is no explicit when-to-use/when-not guidance and no mention of how it relates to alternatives like 'doctor', leaving the agent to infer the boundary.

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