prediction_markets

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

No annotations are supplied, so the description carries the full disclosure burden, and it discloses only the data source (Polymarket) and the metric (implied probability). It says nothing about freshness, how many contracts are returned, whether markets are open/resolved, or that this is a read-only lookup — all of which an agent needs before relying on the numbers.

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?

One front-loaded sentence with no filler, and the examples earn their space by illustrating valid topic phrasing. It is arguably over-compact for a tool with three undocumented parameters, 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?

An output schema exists, so return values needn't be described, and the concept is simple enough that the topic examples carry much of the load. Still, with 0% parameter coverage and no annotations, the absence of any note on 'as_of' (historical snapshots?) or result volume leaves a real gap.

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 description coverage is 0%, so the description must compensate; it does so only for 'topic', where the three quoted examples meaningfully teach the expected phrasing (event/policy/recession style queries). The 'as_of' and 'limit' parameters (default 6) are left entirely unexplained in both schema and description, which is the main gap.

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?

States a specific verb ('implied probabilities') and resource ('Polymarket') with three concrete topic examples, so the agent knows exactly what data comes back. It never distinguishes itself from adjacent siblings like macro_indicator or global_news, which could plausibly cover the same events, so it stops short of a 5.

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 only implied by the example topics ('Fed rate cut', 'recession 2026', a company event), which hints at the kinds of questions suited to it. There is no explicit when-to-use versus when-not, and no mention of preferring this over macro_indicator or company_news for event-driven questions.

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