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

Annotations already declare readOnly, non-destructive and closed-world, so the safety profile is covered. The description adds real scope detail — that it surfaces owned status, approved policy revisions, runs, budget and delivery switches, and that it triggers no work — but says nothing about result size, ordering, or whether all pipelines are returned.

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?

Two short sentences with no filler, and the negative scope clause is placed last where it acts as a boundary. The first sentence is a dense noun-phrase list that is efficient but slightly cryptic to parse.

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?

With no input parameters and no output schema, the description carries the full burden of explaining what comes back. It does enumerate the data categories at a high level, but "budget and delivery switches" and "sample and recurring runs" are too abstract for an agent to predict the response shape.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description's field enumeration is about return content, not inputs.

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 verb is "Read" and the resource is only implied by the tool name; the description enumerates data categories (pipeline status, policy revisions, runs, budgets, delivery switches) rather than stating plainly that it returns a list of pipelines. It does partially distinguish itself from action tools via "Does not run research", but overlaps ambiguously with siblings like read_pipeline_sample and read_pipeline_leads.

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 only guidance is negative: "Does not run research or consume events." That implies a boundary versus run_pipeline_sample/prepare_pipeline, but no sibling is named and there is no positive statement of when to call this rather than the other read tools.

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