Current macro regime + plain-English interpretation of how strong the model's edge is in this regime. CALL THIS when the user asks about market conditions, regime, 'is it a good time to invest', or how confident the model is right now.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It adds useful context about what the tool returns (current regime and edge interpretation) but does not disclose how the regime is determined, data freshness, or any limitations. For a zero-parameter informational tool, this is adequate but not rich; it could mention that it's a snapshot or read-only operation.
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, followed by explicit usage triggers. Every word earns its place, and it is neither verbose nor under-specified. Structure is exemplary for a simple query tool.
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 zero parameters, no output schema, and no annotations, the description covers the main aspects: what it does and when to invoke it. It does not explain the meaning of 'macro regime' or the output format, but for a zero-param tool with clear usage triggers, it is reasonably complete. The plain-English interpretation is highlighted, which addresses the user-facing value.
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 tool has zero parameters, and the schema coverage is 100% (empty schema). The baseline for zero parameters is 4, and the description correctly avoids mentioning parameters since none exist. There is no need to compensate for undocumented parameters.
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 provides 'Current macro regime + plain-English interpretation of how strong the model's edge is in this regime,' which specifies both the resource (macro regime) and the output type (interpretation). It distinguishes from likely sibling tools like tengu_macro or tengu_regime by emphasizing the plain-English interpretation of model edge, though it does not explicitly name alternatives. The purpose is specific and actionable.
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 gives explicit invocation triggers: 'CALL THIS when the user asks about market conditions, regime, "is it a good time to invest", or how confident the model is right now.' This provides clear context for when to use the tool. It does not mention when not to use it or name specific alternative tools, but the triggers are strong enough to guide selection.
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.
