Top-N feature importances for the prediction models (default top 50, optionally filtered to one model). Call it when the user asks 'what is the model actually looking at?' or which inputs are driving current predictions.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| top_n | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It explains the default top 50 and the optional model filter, but does not describe what happens when no model is specified, the format of the returned importances, or whether results are ordered. This is adequate but not rich.
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 with no filler. The action, default behavior, and usage trigger are all front-loaded, making it highly scannable and useful for an AI agent.
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 tool with two optional parameters and no output schema, the description covers the main elements: purpose, defaults, and when to call it. It does not describe the return shape, but the tool name and context make that reasonably inferable. Minor gaps like model filtering behavior are not fully addressed, but overall the description is complete enough for most use cases.
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 input schema has no descriptions at all, so the description must compensate. It does explain the top_n default and the model parameter as an optional filter, but it does not clarify what valid model values are or how top_n interacts with the maximum. The schema provides constraints (min/max/default) but the description adds only partial semantic meaning.
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 Top-N feature importances for prediction models, with a specific default (top 50) and an optional model filter. It also provides concrete user-intent phrases ('what is the model actually looking at?') that help distinguish it from generic tools. However, it does not explicitly distinguish itself from sibling tools like tengu_ml_weights or tengu_v3_intel_factor_importance, which may serve similar purposes.
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 'Call it when the user asks...', giving clear context for when to use this tool. It lacks when-not-to-use guidance or alternatives, but the provided trigger phrases are strong enough to guide an AI agent in selection.
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.
