Aggregated whale-transaction stats over the window: total volume, net exchange flow (in vs out), and biggest single tx. PRIMARY tool for 'is smart money buying or selling this coin?' — use whale_transactions for the individual transfers.
| Name | Required | Description | Default |
|---|---|---|---|
| tickers | No | ||
| date_range | No | last7days |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states that the tool aggregates data and lists the specific metrics returned. While it doesn't mention edge cases like rate limits or data delays, the nature of the tool as a read-only summary is well conveyed. It doesn't contradict anything and adds reasonable context beyond the schema.
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 key function and outputs, and includes a clear call-to-action for alternative usage. Every sentence 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?
For a simple tool with 2 parameters and no output schema, the description is largely complete: it explains the output fields and when to use it. However, given 0% schema coverage, the lack of parameter format details (e.g., whether tickers is comma-separated or date_range options) is a notable gap that prevents a perfect score.
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 has 0% description coverage, and the description does not explain parameter formats. 'tickers' and 'date_range' are only referenced implicitly via 'over the window' and 'this coin', but no details about accepted formats, separators, or allowed values are given. Given the low schema coverage, the description should compensate, but it fails to provide meaningful parameter semantics.
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's function: it provides aggregated whale-transaction stats (total volume, net exchange flow, biggest single tx). It explicitly distinguishes itself from the sibling tool by declaring it the PRIMARY tool for determining whether smart money is buying or selling, and by pointing to whale_transactions for individual transfers.
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 explicitly tells when to use this tool ('PRIMARY tool for 'is smart money buying or selling this coin?'') and when to use an alternative ('use whale_transactions for the individual transfers'). This gives clear usage direction and differentiates it from the sibling tool.
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.
With 336 tools, there is substantial overlap. Over a dozen health/status tools share nearly identical 'is the system healthy?' descriptions (e.g., tengu_status, tengu_ready, tengu_ml_health, tengu_v3_system_health, tengu_v3_stream_status), and multiple single-ticker analysis (tengu_ml_predict, tengu_copilot_score_ticker, tengu_v3_intel_ml_prediction) and top-picks (tengu_copilot_top_picks, tengu_ml_top_picks, tengu_v3_trade_setups) tools have poorly defined boundaries. Agents would frequently misselect.
The server mixes no-version (tengu_crypto), v2 (tengu_v2_drift), v3 (tengu_v3_intel_*), and copilot (tengu_copilot_*) families, and within families there is inconsistent verb/noun ordering (tengu_v3_research_fetch_url vs tengu_v3_news_summary). While subfamilies like tengu_v3_private_markets_* are internally consistent, the overall naming pattern is chaotic and unpredictable.
336 tools is far beyond any reasonable tool set size, even for an all-in-one financial data platform. This extreme count creates choice paralysis, high latency in tool selection, and makes the server effectively unusable for autonomous agents. The calibration guideline marks 50+ as extreme; this is nearly 7x that threshold.
The platform covers a vast domain: equity and crypto prices, fundamentals, insider trading, options, news (including crypto and FX), private markets, streaming data, risk metrics, and execution planning. There are minor gaps (no direct multi-ticker comparison tool, no order placement), but the surface is remarkably comprehensive for an analysis-focused server.
