What drives the model: Fama-French 5-factor loadings showing which systematic factors explain the strategy's returns, plus the ensemble's Bayesian voter posteriors ranking which signals it trusts most (top_n, default 50). PRIMARY tool for 'why does the model like this?' and 'what is the strategy actually betting on?' questions.
| Name | Required | Description | Default |
|---|---|---|---|
| top_n | No |
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 explains the two output components (factor loadings and voter posteriors) but does not clarify whether the tool is read-only, what strategy scope it applies to, or any potential rate limits. This leaves gaps beyond what the name and schema imply.
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 well-structured sentences lead with a concept, list the outputs, and end with clear example use cases. There is no redundant wording, and the description is appropriately sized for the tool's simplicity.
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 single-optional-parameter tool with no output schema, the description covers the key aspects: what it returns and when to use it. It could note that the analysis is for the global strategy (no strategy parameter exists), but the essential information is present and it is likely sufficient for an agent to select this tool correctly.
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 no property descriptions (coverage 0%), so the description's mention that top_n controls 'ranking which signals it trusts most' adds genuine meaning. It also restates the default (50), reinforcing the parameter's purpose and limits.
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 defines the tool's purpose: it returns Fama-French 5-factor loadings and Bayesian voter posteriors to explain model behavior. It explicitly states it is the 'PRIMARY tool' for 'why does the model like this?' and 'what is the strategy actually betting on?'—specific verbs and resources that distinguish it from sibling tools like tengu_v2_feature_importance and tengu_v3_intel_voter_attribution.
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 gives clear context for when to use it via 'PRIMARY tool for...' questions, which is strong guidance. However, it does not mention any alternative tools or explicitly state when not to use it, so it misses the 'when-not' part of a perfect score.
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.
