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

Annotations already declare readOnly, idempotent, closed-world and non-destructive, so the safety profile is covered. The description adds real substance by naming the two values returned (PyPI version and schema revision) and the drift-detection workflow they enable. It adds value but doesn't go beyond what the output schema already carries.

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?

Front-loaded with the core purpose in the first sentence, followed by the return contents and then the use case. The trailing 'without going outside the MCP protocol' clause is slightly padded but does convey a real constraint.

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?

With zero params, rich annotations and an output schema, the description covers everything an agent needs: what it returns, what the values mean, and why it would call it. Nothing material is left to inference.

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 the baseline is 4 and no parameter explanation is needed. The description correctly doesn't waste space on inputs.

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 and resource pair: retrieves the running PyPI version of wspr-mcp plus the wspr.live ClickHouse schema revision. That is clearly distinct from the sibling wspr_* analytics tools, which all query spot data.

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?

Explicitly says to use it 'to confirm fleet alignment across MCP deployments' and to compare service_version and spec_version across servers to detect drift. There's no alternative tool it could be confused with, so the missing explicit exclusion costs little.

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