due_diligenceComprehensive token research: security audit, holder concentration, whale activity, risk score with level (LOW-CRITICAL), and SAFE/CAUTION/RISKY verdict.
| Name | Required | Description | Default |
|---|---|---|---|
| mint | Yes | Token mint address (base58) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description relies on the term 'research' to imply read-only behavior. It lists the analytical outputs but does not disclose operational details such as data sources, latency, or side effects, which are part of the full burden without annotations.
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 purpose ('Comprehensive token research') and then lists specific deliverables. Every word adds value with no redundancy.
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 description adequately conveys what the tool returns by naming the key components (security audit, holder concentration, whale activity, risk score, verdict). Although no output schema exists, it does not specify the exact format or structure of the response, which would be beneficial for a comprehensive tool.
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 coverage is 100% and the only parameter 'mint' is fully described in the schema as 'Token mint address (base58)'. The description adds no additional meaning or usage context for the parameter.
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 performs comprehensive token research and enumerates specific outputs (security audit, holder concentration, whale activity, risk score, verdict). This distinguishes it from sibling tools like enrich_token or whale_watch by emphasizing a holistic due diligence report.
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 use for full token due diligence but does not explicitly state when to use it over alternatives or provide exclusions. No sibling tools are mentioned as 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.