Headline-only alert stream — lighter and faster than full news items; category=general for market-wide or category=ticker with tickers for specific names. Call this when the user wants breaking headlines or the very latest on a name and speed matters more than article bodies.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| items | No | ||
| tickers | No | ||
| category | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It adds useful context: it's headline-only, lighter, faster, and the category parameter filters market-wide vs ticker-specific. However, it omits details about return format, pagination, rate limits, or any side effects. For a read-only stream, this is adequate but not rich.
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 wastes no words. It efficiently covers what it does, how it filters, and when to use it.
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?
Given the tool has 4 optional parameters and no output schema, the description provides the essential purpose, usage guidance, and filter semantics. It doesn't detail the return structure, but that's partially implied by 'headline-only alert stream.' For a relatively simple tool, it's mostly complete, though it could mention pagination bounds.
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 description coverage is 0%, so the description must compensate. It explains the category parameter (general vs ticker) and how tickers relate to category=ticker, adding meaning beyond the enum. However, it doesn't mention 'page' or 'items' parameters, which remain undocumented. Partial coverage, so a mid-range score 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 identifies the tool as a 'headline-only alert stream' that is lighter and faster than full news items, distinguishing it from article-heavy news tools. It provides a specific verb-like purpose (streaming headlines) and scope (market-wide or per ticker), though it doesn't explicitly name sibling 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 this tool: 'when the user wants breaking headlines or the very latest on a name and speed matters more than article bodies.' It also contrasts with 'full news items,' implying alternative tools exist, but it doesn't name specific alternatives.
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.
