feed_latestDaily intelligence brief — pre-computed ranking of trending Solana tokens with composite-signal scoring. Cached 24h, lazy-populated on cache miss. Pass since (ISO 8601) to short-circuit on no-change polls.
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | Optional ISO 8601 timestamp of last successful poll. If brief is not newer, response sets unchanged=true with empty payload. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses caching (24h), lazy population on cache miss, and the short-circuit behavior for unchanged polls. This adds meaningful context beyond what the schema provides, though it omits details like rate limits or return format specifics.
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 two sentences, front-loaded with the core purpose, and contains zero waste. Every clause adds value, from 'daily intelligence brief' to the caching and parameter usage notes.
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 simple tool with one optional parameter and no output schema, the description adequately explains the product type, data source, and polling behavior. It could be more explicit about the exact content of the brief (e.g., token list, scores), but it is sufficient for an agent to understand the core use case.
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?
The schema already documents `since` fully, including the unchanged=true response behavior. The description's 'short-circuit on no-change polls' is semantically equivalent and adds no new information. Since schema coverage is 100%, 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 delivers a 'daily intelligence brief' consisting of a 'pre-computed ranking of trending Solana tokens with composite-signal scoring.' This specific verb+resource distinguishes it from sibling tools like new_tokens or trending_signals, which focus on different aspects.
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 periodic polling of daily trending tokens and instructs to pass `since` to short-circuit on no-change polls. It gives clear context but does not explicitly name alternatives or when not to use this tool, earning a 4 rather than 5.
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.