trending_signalsRanked list of Solana tokens worth paying attention to right now. Composes DexScreener trending + risk scoring + whale-flow into a composite signal with reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of tokens to return | |
| max_risk_score | No | Maximum risk score (0-1) | |
| min_liquidity_usd | No | Minimum liquidity in USD | |
| include_whale_watch | No | Include whale flow signal |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the composite methodology (DexScreener trending + risk scoring + whale-flow) and mentions reasoning. However, it doesn't specify return format, real-time vs cached, or any side effects, leaving some ambiguity.
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?
Two sentences, front-loaded with the core purpose, and every word adds value. It's an appropriately concise description.
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 read-only signal tool with no annotations or output schema, the description explains purpose and methodology but omits details about the output structure (e.g., what 'reasoning' includes) and any limitations. It's adequate but not fully complete.
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 description coverage is 100%, so the baseline is 3. The tool description adds no additional meaning to parameters like 'max_risk_score' or 'include_whale_watch' beyond what the schema already explains.
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 returns a ranked list of Solana tokens with a composite signal. It uses a specific verb ('Composes') and identifies the resource (Solana tokens), but doesn't explicitly distinguish from siblings like 'consensus_signal' or 'feed_latest'.
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?
Usage is implied: use this to get a ranked list of currently notable Solana tokens. However, the description does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives.
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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With 32 tools, several have overlapping purposes, such as wallet_history vs portfolio_history (both track wallet portfolio over time) and smart_money_flow vs smart_money_trenches (both follow smart money movements). However, most tools have clearly distinct scopes, and detailed descriptions help differentiate them.
All tool names follow snake_case with a predictable verb_noun or noun phrase pattern (e.g., enrich_token, compare_wallets, perps_market_trend). The consistent structure makes the set easy to navigate, even the 'perps_' prefix group is uniform.
32 tools is well above the 25-tool threshold, making the surface feel heavy. While the breadth reflects the wide domain of Solana analytics, the sheer number can overwhelm agents and increase the chance of selecting the wrong tool.
The tool set covers most aspects of Solana token/wallet/perp analysis, including enrichment, comparison, trend tracking, smart money flows, and perp market structure. Minor gaps exist, such as no direct historical OHLCV endpoint, but the existing tools handle core workflows well.