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

With no annotations, the description carries the transparency burden. It discloses that theses include entry logic and invalidation criteria, and mentions live price tracking, plus a disclaimer about research framing. It doesn't detail data freshness or rate limits, but for a zero-parameter tool this is acceptable.

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?

Three sentences, each purposeful: definition, usage trigger, and caveat. The description is front-loaded with the core purpose and avoids filler, 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?

For a zero-parameter tool with an output schema and clear sibling differentiation, the description is fully adequate. It covers what, when, and the nature of the output, while the output schema handles return value details.

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 no parameters, so the schema is trivially complete. The description doesn't need to compensate for parameter ambiguity, and no parameter information is required. Baseline 4 for zero-parameter tools.

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 identifies the resource ('research desk theses') and what they contain (thesis, entry logic, invalidation criteria, live price tracking). It distinguishes itself from sibling tools like get_market_odds or get_edge_signals by focusing on research analysis rather than direct market data.

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?

Explicitly states when to call: 'when the user asks where the value is, what to research, or for prediction-market trade ideas.' It also implies not for direct execution by mentioning 'Research framing only' and adds the caveat 'not financial advice.'

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