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

No annotations are provided, so the description carries the full burden, and it does disclose the return payload in detail: the recommended system, the rationale, up to two alternates, related guides, and a next step. What it omits is any explicit statement that this is a side-effect-free read/compute call and where its knowledge comes from (firm profile, resource catalog), leaving a small behavioral 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?

Purpose is front-loaded in the first clause, followed by illustrative examples and then an enumeration of outputs — a logical order. It is a single long sentence with some promotional framing ('the system Artemis GTM would build') that could be tightened, but nothing is redundant.

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 one-parameter, no-output-schema tool the description is nearly self-sufficient: it explains both the accepted input shape and the full return structure. The remaining gap is the absence of any note on side effects or on how this tool relates to book_evaluation and the other siblings.

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 100% for the single 'problem' parameter, so the baseline is 3, but the description adds real value by showing three concrete example problems that calibrate the expected granularity and plain-language register. That goes beyond the schema's bare 'described in plain language'.

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 names a specific input (a plain-language GTM problem) and a specific output (the system Artemis GTM would build, with rationale and alternates), so the agent knows exactly what the tool produces. It's distinguishable from siblings like list_systems (enumeration) and book_evaluation (scheduling), though it never names those siblings to reinforce the distinction.

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?

Usage is implied rather than stated: the examples ('leads sit for two days...', 'our ICP is too broad') make clear this is the entry point when a prospect describes a symptom. There is no explicit when-not clause and no routing to alternatives, even though 'the next step: an evaluation call' clearly points at book_evaluation without saying so.

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