One-call technical read on a ticker: fuses candlestick chart (RSI/MACD/BB), GEX, max pain, options flow/volume, insider and congressional trades, and off-exchange volume into a signal list, aggregate bull/bear stance, and embedded PNG chart. Call this FIRST for 'how does the chart/setup look?' — one round-trip replaces ~8 calls.
| Name | Required | Description | Default |
|---|---|---|---|
| bars | No | ||
| ticker | Yes | Path parameter 'ticker' (required). | |
| interval | No | day |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It clearly communicates that this is a read-only analysis ('technical read') and details both inputs (indicator chart, GEX, max pain, options, insider/congress, off-exchange) and outputs (signal list, aggregate bull/bear stance, PNG chart). It does not discuss latency, cost, error modes, or data freshness, which would make it fully transparent.
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?
Two sentences, front-loaded with the primary purpose, then the use-case guidance. Every phrase adds value—listing data sources, describing output components, and telling the agent when to invoke it. No filler or repeated information from the schema.
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 high-complexity aggregation tool with no output schema and no annotations, the description does a good job of summarizing purpose, inputs, outputs, and usage guidance. It is missing detailed return structure, optional parameter semantics, and any edge-case or limitation notes, but it gives an agent enough context to select and invoke the tool correctly for a first-pass technical read.
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 only 33% (only ticker has a description; bars and interval just have defaults). The description mentions 'candlestick chart' but never explains what bars or interval control. Since schema coverage is below 50%, the description needed to compensate but did not, leaving two of three parameters semantically under-documented.
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 opens with 'One-call technical read on a ticker' and enumerates the fused data sources (RSI/MACD/BB, GEX, max pain, options flow/volume, insider/congressional trades, off-exchange volume). It clearly differentiates from the many individual intel/fundamental tools by emphasizing the 'one-call' aggregation and 'replaces ~8 calls' efficiency, making its specific role unmistakable.
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 phrase 'Call this FIRST for how does the chart/setup look?' provides an explicit when-to-use scenario, and 'one round-trip replaces ~8 calls' signals that this should be preferred over assembling the same data from separate calls. However, it does not name specific alternative tools or state explicit when-not-to-use conditions, so it stops 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.
