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

With no annotations, the description carries the full burden, and it does disclose the return payload (direction, odds, significance flag, model comparison) and that pagination is supported. It stops short of behavioral essentials such as whether this is a read-only call, data freshness/latency, cost, or rate limits, and the return detail is partly redundant against the existing output schema.

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?

Front-loads the invocation condition and then the return shape, which is the right order for an agent. The five quoted trigger examples consume a meaningful chunk of the text and are partly interchangeable, slightly diluting conciseness.

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 a true output schema, the description is not obligated to detail returns, yet it still supplies them, plus usage triggers and all four parameter names, so an agent has enough to invoke correctly. Remaining gaps are operational (auth, freshness, rate limits), which are minor for a read-style board endpoint with zero required parameters.

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, and it does name all four parameters with brief semantics: cursor pagination, limit, min_edge threshold, and field masking. It does not explain units, valid ranges, or defaults (e.g., what edge scale min_edge uses), leaving some ambiguity.

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?

Names a specific resource (open markets ranked by model-vs-market disagreement) and a specific output (Buy YES/NO direction, implied odds, historical significance, multi-model comparison). This is clearly distinguishable from siblings like foresea_market_leaderboard or foresea_scan_markets, which implies ranking/filtering but not model-vs-market edges.

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?

Provides concrete trigger phrasings ("What are the best bets right now?", "Show me the edge board") that map user intent to this tool. However, it never states when NOT to use it or names an alternative sibling (e.g., market_leaderboard or scan_markets) for adjacent intents, so the routing guidance is clear but one-sided.

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