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

Given no annotations, the description carries the behavioral transparency burden. It uses active verbs 'List' and 'Returns' indicating a read-only operation, and adds contextual detail about the most useful contribution, but does not explicitly state a lack of side effects or permissions.

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 packs purpose, usage, and output into two sentences with no filler. Every clause earns its place, including the motivational note about measurement contributions, which clarifies priority.

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?

For a simple read-only tool with one optional parameter and no output schema, the description sufficiently covers what it does, when to use it, and what it returns. This fully equips an agent to invoke it correctly.

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 100%, and the sole 'label' parameter is fully described in the schema. The tool description adds no extra parameter meaning, so the baseline of 3 is appropriate.

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 'List what this project needs done' and specifies it returns open GitHub issues with labels and the location of contribution rules. It distinguishes itself from sibling tools by framing it as the contribution entry point, especially for measurement reproduction.

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 instructs 'Call this when asked to contribute to provinglab.dev or Full Page PDF Snap, or when looking for a measurement to reproduce'. This provides clear, context-rich guidance on when to invoke the tool, with no ambiguity.

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