Uncertainty-discounted Kelly fraction for one ticker — the bet size after haircutting full Kelly for estimation error. Call this when the user asks 'how much should I bet on X?' or to know how large a position the evidence actually supports before sizing a trade.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | Path parameter 'ticker' (required). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It transparently explains the key behavior: the Kelly fraction is discounted for estimation error, and it operates on one ticker. It does not mention return type or edge cases, but the core methodology and scope are well disclosed.
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 a single, well-structured sentence followed by an actionable usage clause. It front-loads the purpose, uses an em dash to explain the concept efficiently, and contains no filler or redundant information. Every element earns its place.
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 simple one-ticker compute tool with no annotations and no output schema, the description covers the purpose, methodology, and trigger scenarios. It falls slightly short by not explicitly stating the return format (e.g., decimal fraction) or handling edge cases like unavailable data, but it is sufficiently complete for practical use.
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 coverage is 100% (only 'ticker'), but the schema description is minimal ('Path parameter ticker (required)'). The tool description adds meaning by explicitly linking 'ticker' to 'X' in the user query and scoping it to 'one ticker', helping agents understand what value to supply. This elevates it above the baseline 3.
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 returns an uncertainty-discounted Kelly fraction for a single ticker, explaining the core concept ('haircutting full Kelly for estimation error'). It distinguishes itself from siblings by focusing on single-ticker bet size and positions the tool with specific user queries like 'how much should I bet on X?'.
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 says when to call the tool ('when the user asks how much should I bet on X' or for position sizing) but does not mention alternatives or when not to use it. This provides a clear trigger condition but lacks exclusions or sibling comparisons, placing it at a 4 rather than 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.
