get_participation

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds meaningful behavioral context by saying the tool deliberately refuses to compute scores/ranks and exposes a 'refuses' list that says what it will not compute and why. This is a genuine non-obvious behavioral trait. It does not explicitly state read-only semantics or side effects, but the content and refusal behavior are well disclosed.

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 compact: two sentences, with the core purpose front-loaded in the first phrase. Each listed output dimension earns its place, and the explicit non-leaderboard clarification is valuable. It is dense and uses some domain jargon, but it is appropriately sized and efficiently organized.

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 there is no input schema to worry about, no annotations, and no output schema, the description does a strong job of telling an agent what to expect: participation vectors, community-shape metrics, scarce verbs, and a refusal list. It could be more complete by explaining the shape of the refuses list or defining terms like 'independence structure', but for invocation purposes with zero parameters it is largely sufficient.

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?

There are zero parameters, so the schema leaves no ambiguity and the description does not need to document parameter semantics. The described output dimensions give the agent a clear sense of what the no-parameter call returns. This matches the baseline of 4 for a zero-parameter tool.

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 identifies the resource and purpose: it reports on who works the register and where it is short-handed, with specific output categories like per-contributor verb vectors and community shape. It also distinguishes itself from a leaderboard by explicitly stating 'no score, no rank', which helps separate it from sibling tools. It stops short of a crisp 'returns X' verb, and some jargon like 'bus-factor concentration' is unexplained, so it is not a perfect 5.

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 implies when to use it: when you need participation structure, community shape, or short-handedness analysis rather than scores or ranks. However, it names no alternative sibling tools and does not explicitly state 'use this when...' or 'use X instead when...'. The 'Deliberately NOT a leaderboard' line gives some exclusion guidance but not enough to fully route an agent among the many sibling tools.

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