Sentiment leaderboard across the full tracked crypto universe — every coin's sentiment_score over the window (default last7days). Call this when the user asks which coins have the most bullish or most bearish news sentiment right now.
| Name | Required | Description | Default |
|---|---|---|---|
| page | 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 burden of transparency. It discloses the scope ('full tracked crypto universe'), the time window ('default last7days'), and that it returns a sentiment score per coin. However, it does not explain pagination behavior, response format, or whether the operation is read-only, leaving some behavioral aspects uncovered.
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 with no fluff. The first sentence defines the purpose and output, and the second provides the trigger scenario. It is well-structured and front-loaded with the essential information.
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 is simple with no output schema, so the description should explain return values clearly. It does mention 'sentiment_score' and a leaderboard, but it does not specify sort order, number of results, or how pagination affects completeness. The ambiguity between 'every coin' and the page parameter further weakens completeness.
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 provides no descriptions for the two parameters (0% coverage), so the description must compensate. It only explains the default for date_range ('last7days') but gives no valid values or format for date_range, and it does not explain the 'page' parameter at all. This is insufficient for a tool where parameters affect data retrieval.
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 identifies the tool as a sentiment leaderboard across the full crypto universe, specifying the resource (crypto coins) and the metric (sentiment_score). It distinguishes itself from siblings by emphasizing 'full tracked crypto universe' and 'every coin', which aligns with the tool's name and differentiates it from per-ticker or market-sentiment tools.
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 states when to use the tool: 'Call this when the user asks which coins have the most bullish or most bearish news sentiment right now.' This provides clear context, though it does not mention any exclusions or alternative tools, so it misses the 'when-not/alternatives' component for a top score.
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.
