AI-generated company analysis for one ticker — summary, strengths, concerns, peer comparison and a quality score. Call this when the user wants a synthesized qualitative read rather than raw numbers. Premium: metered at 100 credits/mo.
| 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, the description carries the transparency burden. It discloses that the output is 'AI-generated' (non-raw, synthesized) and mentions the premium credit metering ('100 credits/mo'), which is a key usage constraint. It doesn't disclose things like data latency or that conclusions may be subjective, but the core behavioral traits are covered.
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 functionality, then usage guidance, then cost. No filler or redundant restatement of the tool name. Every sentence 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?
Given one parameter, no output schema, and no annotations, the description is adequately complete: it explains what the tool returns, when to use it, and notes the credit meter. It could also mention output format or whether the analysis is real-time, but the key decision-making context is present.
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 offers only a tautological 'Path parameter ticker (required).' The tool description compensates by stating 'for one ticker' and 'company analysis,' which clarifies the parameter is a company ticker symbol. For a single obvious parameter, this meets the baseline without needing extensive documentation.
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 it produces an 'AI-generated company analysis for one ticker' and enumerates the output components: summary, strengths, concerns, peer comparison, and quality score. This specific verb and resource list distinguishes it from sibling tools that return raw fundamental data (e.g., tengu_v3_fundamentals_metrics) or other analysis tools like tengu_v3_intel_ml_prediction.
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?
Explicitly says 'Call this when the user wants a synthesized qualitative read rather than raw numbers,' which provides clear when-to-use guidance and implicitly excludes raw-data scenarios. However, it doesn't name specific alternative tools or list when-not-to-use cases beyond 'raw numbers,' so it stops short of full alternative differentiation.
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.
