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

While annotations already declare readOnly, openWorld, and idempotent, the description goes far beyond them by disclosing resolver contracts, safety short-circuits (status:'low_confidence_match'), market-closed behavior, wide-spread tradeability warnings, parent-event extraction, and resolution-rule risk. It even explains the blocking nature of closed-market handling. This is exemplary behavioral disclosure.

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?

The description is extremely verbose, running many long paragraphs with embedded headers and extensive edge-case explanations. While it is front-loaded with a clear opening sentence and contains genuinely valuable information, every sentence does not earn its place—there is substantial redundancy (e.g., both the market-closed and low-confidence paths are described twice in different words). This length makes it hard for an agent to quickly parse.

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?

Since there is no output schema, the description fully carries the burden of explaining return values. It covers response shapes (result.market, result.analysis, result.evidence), resolver confidence contracts, parent_event structures, news fallback fields, and blocking statuses. It even quantifies warning thresholds (e.g., >10pp spread, 24h move vs. model edge). This is as complete as one could reasonably want for a complex research 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?

The schema already covers 100% of the parameters with detailed descriptions including defaults. The tool description adds lots of behavioral context but not much new about the parameters themselves; the market slug/URL/question-text flexibility is already in the schema. The fan-out examples illustrate behavior but don't clarify parameter syntax or constraints beyond what the schema provides.

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 states a clear, specific verb-resource pair: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It immediately differentiates from sibling research tools by naming exact use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and gives a concrete sense of what the tool returns (evidence packet plus market-vs-model comparison).

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?

The description provides explicit use cases ('Use for...') and even gives a conditional directive ('Check this before sizing sports/esports/event-occurrence bets'). However, it does not name alternative tools or state when NOT to use this tool in favor of a sibling, so it lacks a full when/when-not/alternatives structure.

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