keyring_agent_affinity

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

With no annotations, the description carries the full behavioral burden. It indicates a read-only analysis operation through 'Returns,' and states the data source, but it does not explicitly confirm lack of side effects, permissions, or any output limits/pagination behavior. This is acceptable but not thorough.

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 concise and well-structured: two purposeful sentences followed by an args line. The core purpose is front-loaded, with no filler or redundant information.

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 single-parameter analysis tool with no output schema and no annotations, the description covers the input, source, and high-level output. Minor details like exact output shape or aggregation semantics are missing, but these are not essential for a correct call.

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 only parameter 'days' is clearly explained as 'How far back to analyze (default 30)' despite 0% schema description coverage. This fully compensates for the schema gap by giving the parameter meaning and default context.

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 uses a specific verb ('Analyze') and a clear resource ('agent-service affinity patterns from the checkout ledger'), and explains the output: 'which agents use which services most.' It implicitly distinguishes from siblings like list_available and checkout_history by focusing on affinity analysis, but it does not explicitly name alternatives.

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 states intended use cases: 'workstation specialization analysis and context overload detection.' However, it does not explicitly say when to prefer this tool over its siblings or provide exclusion conditions, leaving usage guidance partly implied.

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