generate_tokenomics_model

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

With no annotations, the description discloses async nature, Team tier requirement, and return format ({job_id, status_url}). It does not contradict annotations. Could add details on polling behavior or time estimates, but sufficiently transparent.

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 sentences with no extraneous words. Front-loaded with the main action and key constraints. Every sentence 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?

Covers async nature, return format, tier requirement, and high-level purpose. Missing guidance on how to retrieve results (polling status_url), and doesn't mention that scenarios are optional. Still fairly complete for an async simulation tool.

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% with descriptions for each parameter. The description adds context about 'emission/unlock impact on FDV' but does not explain the scenario enum values (base, bull, bear) beyond the schema. Baseline 3 is appropriate.

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 simulates emission/unlock impact on FDV across scenarios, distinguishing it from sibling tools like get_tokenomics. The verb 'simulate' and resource 'tokenomics model' are specific.

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?

Mentions 'Async, Team tier' implying usage context, but does not explicitly state when to use this tool versus alternatives like get_tokenomics or generate_due_diligence. No exclusion criteria provided.

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