Trending crypto headlines, noise-filtered down to top stories only. Pass ticker to filter to one coin; omit for market-wide trending. Call this when the user asks what the biggest crypto stories are right now. 120s cache.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | No |
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 adds useful traits: 'noise-filtered down to top stories only' and '120s cache' (with data staleness). It does not describe the exact output structure, but the headline content and filtering behavior are conveyed clearly enough for most use cases.
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 three sentences, front-loaded with the core purpose, and every sentence serves a distinct function: what it does, how to filter, and when to call it. The cache note is short but valuable. There is no redundancy or filler, making it highly efficient and scannable for an AI agent.
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 one optional parameter and no output schema, so the description carries the responsibility for explaining return values. It says 'crypto headlines' but does not specify the structure (e.g., list of objects with titles, URLs). However, given the simplicity and the clear cache/filtering notes, the description is largely sufficient, leaving only a minor gap around output formatting.
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 zero description for the 'ticker' parameter (0% coverage), but the description fully compensates by explaining its purpose: 'Pass ticker to filter to one coin; omit for market-wide trending.' It also implicitly marks the parameter as optional, which aligns with the schema showing 0 required parameters. This is a model example of parameter documentation in the description.
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 providing trending crypto headlines that have been noise-filtered to top stories. While the verb is implied rather than explicit, the resource and scope are specific, and the ticker-filtering distinction separates it from other news tools. It differentiates from siblings like tengu_v3_news_trending or tengu_v3_news_crypto_latest by emphasizing the 'top stories only' filter.
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?
Explicitly states when to use it: 'Call this when the user asks what the biggest crypto stories are right now.' It also explains the ticker parameter behavior (filter to one coin vs. omit for market-wide). However, it does not mention alternative tools or when not to use this one, so it misses the 'when-not' clarification that would earn a 5.
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.
