sentiment_socialSocial-sentiment snapshot for one coin: galaxy score (0-100 composite), alt rank (lower is stronger), bullish sentiment %, 24h social volume, social dominance %, and 24h interactions.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Coin symbol, e.g. "BTC". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already signals safe read-only behavior, and the term 'snapshot' aligns with it. The description adds metric-level detail but no additional behavioral context such as rate limits, data freshness, or source scope.
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?
Single sentence, front-loaded with the core purpose and then a compact metric list; no filler or repeated schema content.
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 snapshot, the description lists all returned metrics with relevant units and scales (0-100, %, lower-is-stronger), so an agent can call it and interpret the result without an output schema.
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 covers the only parameter 100% with a clear example ('Coin symbol, e.g. BTC'), so the baseline applies. The description adds no further parameter semantics beyond implying the coin symbol is the required lookup key.
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?
Clearly identifies a social-sentiment snapshot for a single coin and enumerates the specific metrics returned (galaxy score, alt rank, bullish sentiment, volume, dominance, interactions). It does not explicitly contrast with sentiment_twitter or sentiment_fear_greed, but the 'one coin' scope and metric list distinguish it from broader sentiment tools.
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 when-to-use guidance is given. The description never states when to prefer this over sentiment_twitter, sentiment_fear_greed, or other sibling tools, and it offers no exclusion criteria; only the implicit 'for one coin' scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.