llm_team_push

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

With no annotations, the description effectively discloses behavioral details: it sends formatted messages, auto-detects channel type from URL, and supports multiple formats (Slack, Discord, Telegram, generic POST). No destructive or rate-limit info is needed for this push action.

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 yet informative, with clear sections: purpose, channel detection details, and argument description. No extraneous text.

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 the single parameter, no annotations, and an output schema, the description fully covers the tool's purpose, parameter options, and behavioral details, making it complete for agent invocation.

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 has 0% description coverage, but the description adds full semantic meaning by listing the valid values for 'period' ('today', 'week', 'month', 'all'), the default ('week'), and its context as the time period for the savings report.

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 pushes a team savings report to a configured notification channel, specifying the environment variable and channel auto-detection logic. This distinguishes it from siblings like llm_team_report which likely generates the report.

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 used to send reports to channels but does not explicitly state when to use versus alternatives, nor does it provide context on prerequisites or when not to use it.

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