gtm_list_playsEvery play in the workspace with enough context to route on: which product it sells, whether it is active or paused, whether a LinkedIn seat is actually connected (seatConnected: false means the play cannot act, whatever its status says), and how its leads are spread across the pipeline stages. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable nuance beyond that: it explains that seatConnected:false means the play cannot act regardless of its status, and it notes the pipeline stage distribution. This clarifies data semantics that annotations do not cover. It does not mention pagination or empty results, but that is minor for a no-parameter list tool.
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 two sentences with zero filler. The first sentence front-loads the purpose and the key contextual fields, the second confirms read-only behavior. Every word earns its place.
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 no output schema, the description provides the essential return fields (product, status, seatConnected, pipeline stage spread). It does not state the exact structure (e.g., array vs object) or mention limits, but for a simple list with no parameters and read-only annotations, the information is sufficient for an agent to call the tool and interpret the result correctly.
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 and schema coverage is trivially 100%. Per calibration, a baseline of 4 is appropriate for no parameters. The description adds no parameter-specific details because none exist; it correctly focuses on the output.
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 clearly states the tool lists all plays in the workspace, specifying the exact fields provided (product, active/paused, seatConnected, pipeline stage distribution). It distinguishes itself from siblings like gtm_show_play (single play) and gtm_play_stats (statistics) by framing the output as 'enough context to route on.' This is a specific verb+resource with clear scope.
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 the tool is for getting an overview to make routing decisions, but it never explicitly contrasts it with alternatives like gtm_show_play or gtm_play_stats. There is no guidance on when to choose this over a more detailed view or when to avoid it. The agent must infer usage context from sibling names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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