Find the best prediction market for a natural-language question or trading intent. Returns the best cross-venue match, current probability, liquidity, and any arbitrage opportunity between venues.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Natural language question or trading intent, e.g. "Will BTC reach $200k in 2025?" or "Fed rate cut September" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It describes the return values but does not mention side effects, authentication, or rate limits. However, the tool appears read-only with no destructive hints.
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, front-loaded with action, no redundant words. Efficiently conveys purpose and output.
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 simple single-parameter tool with no output schema, the description sufficiently explains what is returned and how to invoke it. Sibling tools cover other aspects.
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 100% (parameter q described). The description adds helpful examples (e.g., 'Will BTC reach $200k in 2025?') that enhance understanding beyond the schema.
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 finds the best prediction market for a natural-language question, with specific outputs (match, probability, liquidity, arbitrage). It distinguishes from siblings like get_arbitrage and get_quote.
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 explicitly says when to use it (for natural-language questions/trading intents) but does not provide when-not-to-use or explicit alternatives. However, the context of sibling tools implies differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: deep link generation, arbitrage detection, historical data, live quotes, unmet demand discovery, and market routing. No two tools overlap in functionality.
All tool names follow a consistent verb_noun pattern with snake_case (execute_url, get_arbitrage, get_history, get_quote, get_unmet_demand, route_market), making them predictable and easy to differentiate.
Six tools is well-scoped for a prediction market surveillance and trading facilitation server. Each tool covers a distinct aspect of the domain without unnecessary bloat or insufficiency.
The tool set covers the full lifecycle from market discovery (route_market, get_arbitrage, get_unmet_demand) to data analysis (get_history, get_quote) to action (execute_url), with no obvious gaps given the server's stated purpose.
