X (up to 99 tweets) + Reddit cross-source sentiment with full engagement metrics (likes, views, retweets, followers, upvotes), AI bull/bear scoring, and an X-vs-Reddit corroboration check. $0.062 USDC. Payment is consumed on execution, including timeouts.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Token or topic to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses key behavioral traits: cost ($0.062 USDC), payment consumed on execution including timeouts (non-refundable), and a volume limit (up to 99 tweets). It could mention whether the tool is read-only or modifies data, but the cost and limit transparency is strong.
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, dense sentence that front-loads the core functionality. It efficiently conveys sources, metrics, scoring, and cost, but the length could be slightly reduced for readability.
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 one parameter, no output schema, and a straightforward purpose, the description provides a complete picture: what the tool does, its inputs, and its cost. Missing details like return format are minor given the simplicity.
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?
With 100% schema coverage, the baseline is 3. The description adds minimal extra meaning beyond the schema's 'Token or topic to analyze'. It does not specify format constraints or examples, but the schema alone is sufficient.
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 the tool's purpose: cross-source sentiment analysis on X and Reddit with engagement metrics, AI bull/bear scoring, and corroboration check. It differentiates from siblings like alpha_brief and alpha_compare by focusing specifically on sentiment.
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 for sentiment analysis on a token/topic but doesn't explicitly state when to use vs alternatives, nor does it provide exclusion criteria or prerequisites. The mention of payment consumption is helpful but not a direct usage guideline.
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.