Get real-time probability for any prediction market outcome. Returns YES/NO probabilities (0-100%), volume, liquidity, and market metadata from Kalshi and Polymarket.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Natural language search query (alternative to market_id) | |
| market_id | No | Market UUID or external_id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing the output data (YES/NO probabilities, volume, liquidity, metadata) and data sources (Kalshi and Polymarket). It does not disclose potential rate limits or auth requirements, but it provides sufficient behavioral context for a read-only data retrieval tool.
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?
Single sentence, front-loaded with the core purpose, no filler.
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 two-parameter tool with no output schema, the description adequately covers the return values and sources. It doesn't specify exact response structure, but it provides enough context for expected use.
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 coverage is 100% (both 'query' and 'market_id' have descriptions), so the baseline is 3. The tool description adds no additional parameter semantics 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 names a specific verb ('Get') and resource ('real-time probability for any prediction market outcome') and distinguishes itself from siblings like get_market_stats and get_sentiment by focusing on YES/NO probabilities, volume, liquidity, and metadata from Kalshi and Polymarket.
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?
It provides clear context for when to use the tool: when real-time probabilities for prediction market outcomes are needed. However, it does not explicitly name alternative tools or state when not to use it, so it earns a 4.
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.
Most tools have distinct purposes, but list_markets and search_markets overlap in market discovery. However, list_markets is for browsing with filters while search_markets is for keyword/natural language queries, so the distinction is clear enough.
All tools follow a consistent verb_noun pattern, with most using 'get_' for data retrieval and a couple using 'list_' and 'search_' for market discovery. The naming is uniform and predictable.
With 9 tools, the server is well-scoped for its purpose of providing prediction market data. Each tool serves a distinct function, and the count is within the ideal range.
The tool surface covers core operations: authentication, market listing/search, real-time probability, historical data, sentiment, trending, aggregate stats, and usage monitoring. A minor gap is the lack of a dedicated market detail endpoint, but get_probability already returns metadata.