Ultimate-engine aggregate decision for one ticker — the top-level verdict aggregated across the engine stack. PRIMARY tool for 'so what's the final call on ?' — call it when the user wants one consolidated decision rather than raw component signals.
| 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 transparency burden. It discloses that the tool aggregates across the engine stack, but does not describe the return format, whether it's real-time, or any side effects. For a read-only decision tool this is adequate but not rich, hence a 3.
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 contains no redundant information. The second sentence adds a clear usage scenario. Every word earns its place, exemplifying concise and well-structured documentation.
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's simple input (one ticker) and lack of output schema, the description explains what it does and when to use it. However, it leaves the response format ambiguous ('verdict' could be a number, rating, etc.). More detail on expected output would increase completeness, but the current level is minimally viable.
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 has only one parameter, 'ticker', with 0% description coverage. The description mentions 'one ticker' but does not explain what a valid ticker is or provide format details. While the mention gives some context, it does not fully compensate for the missing schema description, so a 3 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 states the tool's function: providing the 'top-level verdict' or 'ultimate-engine aggregate decision' for a ticker. It uses a specific verb ('aggregate', 'decision') and resource ('one ticker'), and distinguishes itself as the 'PRIMARY tool' for final calls, contrasting with 'raw component signals.' This effectively differentiates it from the many 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: 'call it when the user wants one consolidated decision rather than raw component signals.' It does not name specific alternatives, but the conditional guidance is clear and actionable. A score of 4 reflects that it lacks explicit 'when not to use' 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.
