pipeline_status

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

No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing about scope, data freshness, time window, or permissions. For a metrics tool with zero annotation coverage, this is a real gap.

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?

A single efficient sentence with the key output (counts and win rate) front-loaded. No waste, though it is arguably terse to the point of under-specification.

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?

An output schema exists, so return values need not be explained, and with no parameters the surface is simple. Still, the description omits what 'pipeline' scopes over and any time-window or filtering assumption, leaving the agent to guess the metric's boundaries.

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 schema defines zero parameters, so there are no parameter semantics to clarify; the baseline for a parameterless tool applies. Nothing in the description is needed to compensate.

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 concrete verb ('Show') and a specific resource ('pipeline counts and win rate'), so the agent knows it returns aggregate pipeline metrics. It does not, however, differentiate itself from nearby siblings such as earnings_summary or platform_roi, which likely also surface aggregate numbers.

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?

There is no guidance on when to reach for this tool versus the many overlapping analytics siblings (earnings_summary, platform_roi, sla_status, win_journal). The agent is left to infer that this is the general pipeline-health view.

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