Prediction market intelligence (Polymarket + Kalshi + Twitter + Grok). $0.03 USDC. Payment is consumed on execution, including timeouts.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Prediction query (e.g., "bitcoin 100k", "fed rate cut", "election") | |
| category | No | Filter by category |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cost ($0.03 USDC) and that payment is consumed even on timeouts, which is important behavioral information. With no annotations, this is good but could include more details like error handling or data freshness.
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?
Extremely concise: two sentences, no filler. Front-loaded with purpose, followed by cost. Every word adds value.
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?
Given no output schema, the description should hint at return format. It does not. Schema covers inputs fully, but output structure is missing, leaving agents to infer. Could be improved.
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% with good descriptions for both parameters. The description adds no extra meaning beyond the schema, so baseline score 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 states it provides prediction market intelligence from multiple sources (Polymarket, Kalshi, Twitter, Grok), distinguishing it from sibling tools like alpha_sentiment or alpha_news. The mention of cost adds clarity.
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?
No explicit guidance on when to use this tool versus alternatives. It implies use for prediction market queries but does not specify when not to use or suggest sibling tools for other needs.
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 targets a distinct aspect of crypto alpha research (e.g., brief, calendar, compare, deep, macro, memecoin, narrative, news, onchain, perps_funding, portfolio, prediction, risk, search, sentiment, stats, subscribe, token, trending). Descriptions clearly differentiate purposes, minimizing ambiguity.
All tool names follow a uniform 'alpha_{descriptive_noun}' pattern with snake_case, making naming predictable and easy to navigate.
With 19 tools spanning a broad range of crypto intelligence (market data, sentiment, on-chain, risk, portfolio, news, etc.), the count is well-scoped for the server's purpose—neither too few nor excessive.
The tool set covers most key areas of crypto research (price, sentiment, on-chain, risk, news, calendar, narratives, portfolio, predictions, subscriptions). Minor gaps like a dedicated volume/anomaly tool are absent, but the set is largely comprehensive.