get_prediction_market_catalystsUpcoming dated events on Polymarket whose odds are moving — the catalysts most likely to move crypto or macro narratives next.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum rows to return (1-25). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, establishing a safe read operation. The description adds that the data is limited to upcoming dated events with moving odds, but it does not disclose additional behavior like sort order, pagination, or how 'moving' is defined. This meets the baseline given annotation coverage.
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 a single, info-dense sentence that defines both the data source and the selection criteria. There is no redundancy or 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 list tool with one optional parameter and no output schema, the description sufficiently conveys the core behavior. It doesn't specify return format or the threshold for 'moving' odds, which would further improve completeness, but it is not critically incomplete.
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 only parameter, 'limit', is fully documented in the input schema (default 10, max 25). The description adds no additional parameter meaning, but the schema coverage is 100%, so the baseline of 3 applies.
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's output: upcoming dated Polymarket events with moving odds, framed as narrative-moving catalysts. This distinguishes it from sibling tools like get_prediction_market_yields or get_top_movers by specifying the unique resource (catalysts) and scope (Polymarket, moving odds).
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 a clear use case (identifying narrative-moving catalysts) and provides context, but it does not explicitly contrast with alternatives such as analyze_prediction_markets or get_market_sentiment. There are no exclusions or when-not-to-use statements.
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.
With 40 tools, many share overlapping domains: get_smart_money_flow vs get_smart_money_inflow, scan_launchpads vs get_launchpad_radar, track_whales vs get_hyperliquid_whales, and check_token_safety vs analyze_token_security. The detailed descriptions help, but the boundaries are not always clear, making misselection likely.
The tool names employ a wide variety of verbs (get_, analyze_, scan_, track_, find_, generate_, recommend_, run_, list_, ask_, assess_, detect_) with no consistent pattern. While all use snake_case, the inconsistent verb choices and occasional deviations like forecast_chart prevent predictability.
40 tools is well above the typical 3-15 well-scoped range and exceeds the 25+ threshold for 'too many'. While the broad 'crypto intelligence' purpose justifies some breadth, the sheer number makes the surface unwieldy and suggests a lack of focused scoping.
The tool set covers a wide range of crypto intelligence domains: market analysis, forecasting, whale tracking, yield/arbitrage, security checks, prediction markets, backtesting, and even content generation. Missing operations are minor (e.g., no direct portfolio management), but core analysis and data retrieval workflows are well represented.