Out-of-sample model performance — Sharpe, IC, conformal coverage, factor-decomp alpha. CALL THIS when the user asks 'how do I know this works', 'what's your track record', 'is this real alpha vs factor exposure'. Covers out-of-sample backtest, strict-OOS, conformal-validation and factor-decomposition metrics, refreshed weekly.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the tool's coverage (out-of-sample backtest, strict-OOS, conformal-validation, factor-decomposition) and refresh cadence ('refreshed weekly'), adding valuable context beyond the name. It does not, however, specify the output format or any potential side effects, though for a read-only performance report this is acceptable.
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 concise and well-structured. It opens with the core concept, then gives actionable trigger phrases in caps, and ends with coverage details and refresh frequency. Every sentence earns its place with no redundancy or fluff.
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 zero-parameter tool with no output schema, the description is nearly complete. It covers what the tool does, when to use it, the specific metrics included, and the refresh interval. The only gap is that it does not explicitly describe the return format, but given the nature of a performance summary, this is a minor omission.
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 an empty input schema, so schema coverage is trivially 100%. The description adds no parameter semantics because there are none to describe. Per the rubric, a zero-parameter tool gets a baseline of 4.
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 purpose: 'Out-of-sample model performance — Sharpe, IC, conformal coverage, factor-decomp alpha.' It uses a specific noun phrase that identifies the resource and scope, and includes trigger phrases like 'how do I know this works' that pinpoint when an agent should call it. This differentiates it from sibling tools such as tengu_copilot_live_ic_drift or tengu_copilot_decision_review.
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?
Usage guidance is explicit: 'CALL THIS when the user asks...' followed by concrete example questions. This provides clear context for when to invoke the tool. However, it does not mention alternative tools or when not to use it, so it falls short of 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.
