events_prediction_oddsMarket-implied prediction-market odds for a topic (e.g. 'Fed rate cut', 'bitcoin 100k'). Returns the most active matching markets with per-outcome probabilities, volume, and resolution date.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Topic to match against market questions, e.g. 'Fed', 'bitcoin', 'ETH ETF'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds meaningful behavioral context by stating it returns the most active matching markets and enumerating the returned fields, giving the agent a clear sense of behavior beyond the 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?
The description is two sentences with no filler. It front-loads the core purpose, provides concrete examples, and immediately states the output content, making it efficient and well-structured.
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 single-parameter, read-only tool with no output schema, the description provides sufficient context: what the tool does, how the query is used, and what the response will contain. No missing information is critical for correct invocation.
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%, so the schema already documents the query parameter thoroughly. The description adds illustrative topic examples, but does not materially extend parameter meaning 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 clearly identifies the tool as returning prediction-market odds for a topic, with specific output elements (per-outcome probabilities, volume, resolution date). It is easily distinguished from siblings like market_quotes or funding_current by its unique focus on prediction markets despite not naming an alternative.
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 implies usage: call it when you need market-implied probability odds for a given topic. However, it does not explicitly state when to prefer this over other market-data tools or mention any exclusions, so usage guidance is only implied.
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.
Tools are grouped by clear prefixes and mostly target distinct resources; market_quotes vs research_token_view, market_top_movers vs market_trending, and funding_current vs market_quotes have some field or purpose overlap, but descriptions draw enough scope boundaries for an agent to choose correctly in most cases. No two tools are truly interchangeable.
All 30 tool names use lowercase snake_case with a consistent domain-prefix convention such as market_, flow_, research_, sentiment_, and catalysts_, making the surface predictable. Even helpers like utc_time and risk_position_size fit the same noun-oriented pattern without style mixing.
At 30 tools this set crosses the 'too many' threshold, and several tools reproduce data already available through broader ones such as research_token_view and market_quotes. The breadth is defensible for a crypto-research platform, but the surface feels heavy and could be consolidated.
The tool set covers the read-only crypto research workflow thoroughly: market data, candles, derivatives, funding, OI, order book, whale flows, ETF flows, on-chain metrics, news, sentiment, prediction odds, technicals, regime/positioning scans, and position sizing. It also includes health and UTC helpers that close practical workflow gaps, with no obvious dead ends.