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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds substantial behavioral context beyond that: pagination behavior ('complete false means the walk stopped at its page cap'), storage ordering ('runs are not stored in time order'), and relative cost ('most expensive read here'). This transparency meaningfully informs an agent about side effects, limitations, and performance.

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 a lot of information into a tight, forward-loaded structure: scope/ordering, optional filters, an example, return shape, and a critical performance warning. Every sentence serves a purpose; there is no filler or redundancy.

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 4-parameter list tool with no output schema, the description fully covers the return format ({runs: [...], count, complete}), explains the complete flag and its implications, gives a concrete example, and communicates the cost/ordering caveat. An agent has everything needed to call it correctly and interpret results.

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 coverage is 0% in the structured sense, but the description explicitly mentions all four parameters (dataset_id, status, mode, limit) and adds semantics: 'narrowed to one dataset, one status or one mode' and the example clarifies usage. It does not fully explain limit's role beyond the example and the page-cap reference, but it adds enough value over the enum schemas.

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 states a specific verb and resource: 'List this workspace's runs', and adds scope ('workspace's own') and ordering ('newest first'). It also clarifies the narrowing options (dataset, status, mode), which distinguishes it from get_run, run_events, and query_run. The title and description are consistent, and the example reinforces purpose.

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 gives clear when-to-use guidance: 'Use it to find a run_id you lost, or to see what a dataset has been doing.' It also warns when not to use a workspace-wide list ('ALWAYS pass dataset_id when you know it') due to cost and likely incompleteness. However, it does not explicitly name alternatives like get_run for a known run_id, so it stops short of full when-not guidance.

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