Social sentiment data: Reddit crypto trending (ApeWisdom), Binance futures long/short ratios, open interest history, taker buy/sell ratio.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (1-50, default 20) | |
| action | Yes | ||
| filter | No | Subreddit filter for reddit_trending (default: all-crypto) | |
| period | No | Period for Binance endpoints (default: 4h) | |
| symbol | No | Futures symbol for Binance endpoints (default: BTCUSDT) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It lists data types but does not disclose behavioral traits like data freshness, rate limits, or whether it requires authentication. The tool is clearly read-only, but that is not explicitly stated.
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 concise sentence that efficiently lists key data sources. It is front-loaded with 'Social sentiment data', which is the core purpose. However, it could be structured to separate the data categories more clearly.
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?
The tool has 5 parameters, multiple action enums, and no output schema. The description is minimal and does not explain how each action works, what data is returned, or how parameters like 'filter' and 'period' affect results. More detail is needed for an agent to use this tool 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?
Input schema covers 80% of parameters with descriptions (e.g., limit, action, filter, period, symbol). The description adds context about the data sources but does not elaborate on parameter behavior beyond the schema. Baseline score of 3 is appropriate given the 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 that the tool provides 'Social sentiment data' and lists specific data sources (Reddit trending from ApeWisdom, Binance futures metrics). This distinguishes it from siblings like 'social_trending' or 'crypto_derivatives' by specifying exact sources, but it could be more explicit about the aggregation of social and derivatives data.
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 guidance on when to use this tool versus alternatives such as 'crypto_derivatives' or 'social_trending'. The description simply lists available data without context on use cases, prerequisites, or situations where another tool would be more appropriate.
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.
Multiple tools overlap in providing crypto price data (crypto_price, crypto_market, crypto_derivatives, global_assets) and news (news_feed, tradfi_news). While descriptions clarify some differences, agents may struggle to choose between similar tools.
Tool names mix styles: some are simple nouns (backtest, cn_market), others compound nouns with underscores (crypto_derivatives, derivatives_sentiment). No consistent verb_noun pattern, making it harder to infer purpose from name alone.
With 19 tools covering a broad domain (crypto, DeFi, macro), the count feels slightly high but justifiable. However, several tools could be merged to reduce overlap and improve navigability.
The tool set covers a wide range of market intelligence needs: price data, technical analysis, sentiment, macro indicators, news, and DeFi. Minor gaps like on-chain analytics or direct trading are acceptable given the intelligence focus.