Detect and track active market narratives — AI tokens, RWA, L2, memecoins, DePIN. $0.05 USDC. Payment is consumed on execution, including timeouts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of narratives to return (default: 5) | |
| query | No | Free-text narrative query | |
| narrative | No | Narrative slug (ai, rwa, meme, l2, depin, defi, gaming) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral disclosure. It only notes the payment and timeout behavior, omitting details such as side effects, rate limits, idempotency, or what happens on invalid input. The lack of output schema means return format is also not described.
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 long with no fluff. It front-loads the core purpose and then adds a cost note. It could be slightly more structured (e.g., separating purpose from cost), but it is concise.
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 that the tool has three optional parameters and no output schema, the description falls short. It does not explain the return format, error handling, pagination, or other aspects essential for an agent to use it effectively.
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?
All three parameters are documented in the input schema with descriptions (100% coverage). The tool description adds no further semantic value beyond listing example narrative slugs, so it meets the baseline for high schema coverage.
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 'Detect and track active market narratives' and lists specific examples like AI tokens, RWA, L2, memecoins, DePIN. This verb+resource combination distinguishes it from sibling tools like alpha_memecoin or alpha_trending.
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 mentions a cost of $0.05 USDC and that payment is consumed on execution including timeouts, but it does not provide explicit guidance on when to use this tool versus alternatives like alpha_search or alpha_sentiment.
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.