copy_trade_signalsAnalyze a wallet's trading performance: win rate, PnL, consistency, hold time, and smart_money classification.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Solana wallet address (base58) | |
| lookback_days | No | Days to analyze (1-90) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It lists analysis dimensions but fails to mention read-only nature, data sources, or whether the tool produces a signal or recommendation. It does not describe any side effects or limitations.
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, well-structured sentence that front-loads the primary purpose ('Analyze a wallet's trading performance') and then lists specific metrics. It is concise with no wasted words.
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 no output schema and no annotations, so the description needs to explain what to expect. It lists analysis areas but omits the output format (e.g., does it return a signal, a score, or raw metrics?) and how it differs from related wallet tools. This leaves significant gaps for an agent to select and invoke the tool correctly.
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% for both parameters (address and lookback_days). The description does not add much beyond the schema, but it does mention the trading metrics, which are more about outputs than parameter semantics. Since schema already documents parameters well, a baseline of 3 is appropriate.
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 analyzes a wallet's trading performance with specific metrics (win rate, PnL, consistency, hold time, smart_money classification). It uses a specific verb ('Analyze') and resource ('wallet's trading performance'), but does not explicitly differentiate from sibling tools like wallet_history or compare_wallets.
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 evaluating a wallet's trading performance, which fits the tool name 'copy_trade_signals', but it does not explicitly state when to use this tool over alternatives or provide exclusions. No sibling tool comparisons or context are given.
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.